Tag Archives: memory

While you were sleeping: Neural reactivations promote motor learning

Originally published on the PLOS Neuroscience Community

Do you recall that moment when you first learned to ride a bike? After days of practice, it finally clicked. Almost effortlessly, your legs cycled in perfect harmony as you maneuvered gracefully around turns and maintained impeccable balance. You may have learned this skill decades ago but it will likely stick with you for the rest of your life. How did your brain accomplish this remarkable feat of transforming a series of forced and foreign actions into an automatic, fluid movement sequence? A new PLOS Biology study by Dr. Dhakshin Ramanathan and colleagues explored the neural substrates of motor memory acquisition, reporting that reactivation of task-related neural activity patterns during sleep promotes motor skill learning in mice.

Sleep enhances motor learning

To evaluate motor learning, the researchers trained mice to perform a task in which they had to reach for a food pellet. Since sleep is known to be important for memory consolidation, the mice were allowed to sleep before and again after the reach task. The mice performed the task a second time after sleeping to assess how sleep affected their performance. Accuracy on the reaching task improved over the course of the first training period, whereas the mice responded more quickly after sleeping; hence, “online learning” (during the task) improved accuracy and “offline learning” (while sleeping) improved speed. No changes occurred when the mice were sleep-deprived between task sessions, pinpointing sleep – rather than the passage of time – as the source of the performance boost.

Behavioral paradigm (Ramanathan et al., 2015)

Behavioral paradigm (Ramanathan et al., 2015)

Sleep-dependent neural changes

The researchers next explored the neurophysiological basis for this sleep-dependent learning, recording neural activity from the forelimb area of motor cortex. As co-author Dr. Karunesh Ganguly explained,

“Studies had previously studied hippocampal-based memory systems. It remained unclear specifically how the motor system (i.e., procedural memory) processes memories during sleep.”

When the mice performed the task the second time, after sleeping, the onset of neural firing (time-locked to the reach) peaked earlier, and this activity was more strongly modulated by the task. Notably, firing onset did not change after sleep deprivation, confirming that sleep was necessary for this temporal shift in the neural response.

Learning-related reactivation

Elsewhere in the cortex and hippocampus, during rest neurons will fire in a particular sequence matching the temporal pattern during a prior experience, an event known as “replay” that is thought to support formation of memory for that experience. The researchers speculated that reactivation or replay in motor cortex may similarly promote motor learning. Neural activity patterns identified from the reach task were more prevalent during sleep after the task, showing – as predicted – reactivation of task-related activity after motor learning. When mice performed the reach task on multiple days, over the course of all days the degree of reactivation during sleep correlated with reduced reaction time, linking stronger neural reactivation with behavioral improvements.

A) Reactivation of task-related neural activity after learning. B) Reactivation correlates with improvements in reaction time. (Ramanathan et al., 2015)

A) Reactivation of task-related neural activity after learning. B) Reactivation correlates with improvements in reaction time. (Ramanathan et al., 2015)

Since the authors observed neural reactivation during motor learning, they next wondered whether the temporal sequence of this reactivation may be an important element of the memory code. The neural activation pattern during sleep more closely matched the task-related activity pattern after learning than before, although some of the temporal information in the sleep sequence was lost. Although prior studies have shown a role for hippocampal or cortical replay in memory consolidation, Dr. Ganguly raises the important distinctions that here, they “did not find evidence of ‘replay’ (i.e., sequences) but ‘reactivation’ (i.e., synchronous bursts).”

Learning-related plasticity was evident even at the single neuron level. Those neurons with the highest task-related activity were most strongly reactivated during sleep, and those showing the strongest reactivation also happened to show the most dramatic shift in the onset of their response to reaching. The authors speculate that this increased temporal coupling of neural activity to the task could facilitate binding the neurons into a distributed “movement complex” that aids formation of the motor memory.

Locking to spindles and slow waves for widespread plasticity 

Burst of high-frequency activity – known as spindles – and slow wave oscillations have both been implicated in offline learning. If task-related reactivations during sleep are important for memory consolidation, the authors reasoned, they may be temporally linked to spindles or slow waves. After learning the reach task, reactivations were in fact more closely time-locked and phase-locked (i.e., occurred at a particular phase of the cycle) to fast spindles. Reactivations were also more strongly time-locked and shifted their phase-locking to slow oscillations. Thus, during sleep, neural activation patterns related to the motor task were not only more prevalent after training, but their timing was also refined to coincide with particular neural events that may facilitate memory formation. Since spindles may be involved in synchronizing long-range cortical activity, locking task-specific reactivations to spindles could tie them into neuroplastic changes throughout widespread brain networks supporting consolidation.

At a recent talk on sleep-dependent memory consolidation, the speaker compared the neural reorganization that takes place during memory formation to a house renovation. Just as it’s more comfortable and effective for us to check into a hotel while our house is renovated, learning may be more effective when the brain checks out – into the quietude of sleep – while neural reorganization occurs. This may explain why sleep is so important for learning, yet it doesn’t explain how the brain stores new memories during sleep. Past studies have identified neuronal reactivation, coordinated with spindles and slow waves, as critical for forming declarative memories. Dr. Ramanathan and colleagues’ findings suggest that these mechanisms also occur in the motor cortex to support a radically different form of memory – the kind that helped you learn to ride a bike many years ago.

Clarifying the neural dynamics of sleep-dependent learning holds profound implications not just for those of us hoping to learn to play a new instrument or refine our dance moves. It may also help us better understand the remarkable neuroplasticity that underlies rapid motor learning during early development, and hold potential to promote recovery from motor impairments following brain injury. Dr. Ganguly is optimistic about the possible applications of their findings:

“Motor learning is likely an essential process during rehabilitation. Surprisingly little is known about the role of sleep and replay during recovery. With further study, one could imagine using sleep and offline processing to maximize the learning during rehabilitation.”

References

Ji D, Wilson MA (2007). Coordinated memory replay in the visual cortex and hippocampus during sleep. Nat Neurosci. 10:100-107. doi: 10.1038/nn1825

Ramanathan DS, Gulati T, Ganguly K (2015). Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation. PLOS Biol. doi: 10.1371/journal.pbio.1002263

Stickgold R (2005). Sleep-dependent memory consolidation. Nature. 437:1272–1278. doi: 10.1038/nature04286

Image credit https://www.flickr.com/photos/echoforsberg/

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#PLOS #SfN15 Recap: Hidden variables of behavior

Originally published on the PLOS Neuroscience Community

The Society for Neuroscience meeting is unique in both is breadth and depth. There are sessions on literally everything Neuro, each delving with exquisite detail and nuance into their given topic. While this level of focus is great for those seeking comprehensive coverage of their niche, it can be daunting for those looking for a broader sampling of the field’s cutting edge. The Hidden Variables of Behavior symposium was one of the rare sessions to stray from the single-track convention to elegantly bridge seemingly disparate topics, methodologies and applications, producing a standout session with exceptionally broad appeal. It accomplished this by exploring a theme that is perhaps the unifying motivation underlying nearly all Neuroscience research: how does the brain engender behavior? How does neural activity give rise to the thoughts, interactions with our environment, and engagements with others that define our experiences? In an enthralling series of talks by Loren Frank, Mark Schnitzer, Yang Dan and Catherine Dulac, the symposium covered topics ranging from learning and memory to sleep and social behavior. This session had it all.

Rapidly alternating representations of present and past in hippocampal-cortical networks

frankLoren Frank kicked off the symposium by exploring how the hippocampus supports our ability to remember the past and plan for the future. Hippocampal cells have a remarkable ability to replay past experiences via high-frequency oscillations during sharp waves known as ripples. When an animal traverses a path its hippocampal neurons will fire in a characteristic sequence that codes its trajectory; later, at rest or while sleeping, this spiking sequence will repeat, with the sequence sped up approximately twenty times the original rate! Disrupting hippocampal ripples impairs sequence learning, indicating that they’re critical for acquiring memories. However, the mechanisms, at both regional and whole-brain levels, by which sharp wave ripples (SWRs) help to consolidate memories are unclear.

Place_Cell_Spiking_Activity_Example

Much attention has been paid to neurons in hippocampal subregions CA1 and CA3, which are excitable by high-speed motion and positively modulated by SWRs. However, Frank’s group identified a new group of hippocampal neurons – CA2P and CA2N – that are also positively and negatively modulated by SWRs, respectively. Notably, the CA2N population has an exceptionally high level of baseline activity and preferentially fires during rest or low speed motion. Because of their distinct function, these rebellious cells may be crucial for ongoing processing of the current state while maintaining representations of the past and future.

Although some (including yours truly) may hold a hippocampo-centric view of memory, Frank reminds us that memory is “not just a hippocampal thing.” Looking to the rest of the brain, his group found that SWRs recruit 35% of prelimbic regions, including cells that are both excited and inhibited by SWRs. Similar to the distinct populations of CA2P and CA2N cells, prefrontal cortex neurons may activate during either high-speed motion or immobility. This balance of excitation and inhibition in the hippocampus and surrounding cortex may promote rapid transitions between representations of the past and future, and facilitate their integration for learning and planning.

Large-scale ensemble neural dynamics underlying learning and long-term associative memory

schnitzer_mark_107Mark Schnitzer continued with this theme, presenting intriguing findings regarding the spatiotemporal properties of neural adaptations subserving learning. However, equally impressive are the advanced imaging tools his lab is developing to explore these issues. Their techniques allow neural recordings in behaving animals at unprecedented spatial depths and extents over long time scales. For instance, their current best is recording 1202 hippocampal cells in a freely moving mouse. Someone give this man the “I-recorded-the-most-neurons” award!

Using these tools, Schnitzer has been exploring hippocampal morphological and physiological changes that contribute to learning. CA1 neurons are a likely target for spatial learning, as they show place-cell activity, preferentially responding to particular regions of an animal’s environment. Surprisingly, dendrites in subregion CA1 are remarkably stable, suggesting that dendritic plasticity is unlikely to be the critical factor underlying learning. However, CA1 spine turnover is relatively rapid – on the order of 8-10 days – in comparison to cortical spines, of which 50% are permanent over a month. Schnitzer explained that although these cells are temporally stochastic in that they sometimes take breaks from their place-coding activity, when they return to the neuronal “spatial ensemble” they always return to encode the same place. What’s more, CA1 spatial representations are refined by learning, becoming both more accurate and reliable in their coding. I’ll be eagerly following Schnitzer’s work to see how their ongoing methodological innovations and applications advance our understanding of the hippocampal dynamics supporting long-term memories.

Neural circuits for sleep control

YIR_RH_DanYang Dan turned from this fast-paced discussion of rapid neural plasticity, spatial navigation and learning to examine neural regulation of sleep. Historically, neurons that trigger alertness and waking have been easy to identify, but researchers have struggled to track down those “sleep neurons.” Past lesion and c-fos studies have shown that hypothalamic – particularly preoptic – neurons are important for inducing sleep.

Combining optogenetics with electrophysiology, Dan’s lab has expanded upon these findings to pinpoint both the responsible cell types and their specific sleep-inducing effects. In particular, activating GABAergic preoptic cells projecting to the tuberomammillary nucleus (also of the hypothalamus) promotes non-REM sleep initially, and REM sleep later. The midbrain’s ventrolateral periaqueductal gray also promotes sleep, but only the non-REM type. Dan’s findings together suggest that mutual inhibition across these key hypothalamic and brainstem regions regulates transitions across three general brain states of waking, REM sleep and non-REM sleep.

Long-term changes in the representation of social information in the mouse medial amygdala

DulacAfter all this talk about sleep, my hypothalamic sleep neurons had begun batting the morning’s adenosine antagonists. Fortunately, Catherine Dulac’s captivating talk exploring the bases of social interactions and sex-specific behavior kept me alert and engaged. Two key circuits working in concert to process social information, she explained, are the olfactory and vomeronasal systems. This latter system in particular may act as a switch to promote appropriate (and suppress inappropriate) sex-specific behavior.

Dulac’s research, fusing molecular, genetic and electrophysiological techniques, has identified the medial amygdala as a critical stop along the vomeronasal circuit for mediating sex-specific social signaling in mice. Medial amygdalar encoding of social cues is not only sexually dimorphic, but is also regulated by salient social experiences including mating and co-housing. Furthermore, the efficiency of medial amygdalar signaling also changes after mating in a sex-specific manner, increasing in males but decreasing in females. Together, Dulac’s work has pinpointed the medial amygdala as an indispensible hub within an extensive neural circuit that regulates social behavior and in turn, is modulated by sexual and social experience.

Every SfN has at least one session that reminds me why I love the brain and re-ignites my passion for Neuroscience. This year, the Hidden Variables of Behavior symposium was it! It may be a year away, but I’m eagerly awaiting #SfN16 for similarly inspiring talks.

For an abbreviated play-by-play, visit my Storified live-tweeting of the symposium’s highlights.

References

Anderson EB, Grossrubatscher I, Frank L (2015). Dynamic Hippocampal Circuits Support Learning- and Memory-Guided Behaviors. Cold Spring Harb Symp Quant Biol. 79:51–58. doi: 10.1101/sqb.2014.79.024760

Attardo A, Fitzgerald JE, Schnitzer MJ (2015). Impermanence of dendritic spines in live adult CA1 hippocampus. Nature. 523:592–596. doi: 10.1038/nature14467

Bergan JF, Ben-Shaul Y, Dulac C (2014). Sex-specific processing of social cues in the medial amygdala. eLife. 3:e02743. doi: http://dx.doi.org/10.7554/eLife.02743

Brennan PA (2001). The vomeronasal system. Cell Mol Life Sci. 58(4):546–555.

Ego-Stengel V, Wilson MA (2010). Disruption of ripple-associated hippocampal activity during rest impairs spatial learning in the rat. Hippocampus. 20(1):1–10. doi: 10.1002/hipo.20707

Weber F, Chung S, Beier KT, Xu M, Luo L, Dan Y (2015). Control of REM sleep by ventral medulla GABAergic neurons. Nature. 526:435–438. doi: 10.1038/nature14979


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Astrocytes may Hold the Key to Exercise-Induced Cognitive Enhancement

Originally published on the PLOS Neuroscience Community


Forget expensive pills or exotic miracle supplements. Exercise may be the most effective – not to mention free and accessible – cognitive enhancer on the market. Research in humans has shown that physical activity can improve cognitive function and may help stave off dementia, yet the biological mechanisms underlying these benefits aren’t fully understood. Animal studies have made substantial progress on this front, demonstrating such positive responses to running as enhanced neurogenesis and elevated levels of neural growth factors. However, much of this research has been relatively narrowly focused, with particular attention devoted to neuronal changes and one notable brain region – the hippocampus. The hippocampus is selectively important for certain functions like learning and episodic memory, but exercise improves a range of cognitive processes, many of which depend on other, non-hippocampal brain regions. Therefore, researchers from Princeton University looked beyond the hippocampus and neurons to more thoroughly characterize the neural events that impart cognitive protection from physical activity. In their study recently published in PLOS ONE, Adam Brockett and colleagues report that running enhances performance on various cognitive tasks, improvements which may be mediated by changes in astrocytes, the lesser appreciated brain cells.

Running selectively boosts some cognitive functions

To manipulate levels of physical activity, rats were divided into a group of runners who were allowed free access to running wheels for 12 days, and another group of sedentary controls. Prior studies have shown that running improves performance on tasks requiring the hippocampus, like learning and memory. Here, the runners and non-runners were subjected to three tests to determine how exercise affects cognitive functions that are not dependent on the hippocampus. An object-in-place task, which tests how well rats remember the location of previously encountered objects, relies on the medial prefrontal cortex, hippocampus and perirhinal cortex. A novel object task, in which rats distinguish novel from familiar objects, selectively depends on the perirhinal cortex. Lastly, a set-shifting task, supported by the orbitofrontal and medial prefrontal cortices, measures attention and cognitive flexibility.

Compared to their non-runner companions, the runners performed better on the object-in-place test and on several measures of the set-shifting task. However, there were no differences between runners and non-runners in performance on the novel object recognition test. Of course, the cognitive benefits of running don’t end here, since many cognitive domains were not assessed in this test battery. But these findings highlight a striking selectivity of the brain-boosting powers of exercise. In particular, they suggest that running may enhance functions that specifically depend on the medial prefrontal and orbitofrontal cortices, along with the hippocampus, but it does not appear to modulate perirhinal-dependent functions.

Cognitive enhancement is linked to astrocytes

Although behavioral changes provide a window into the underlying neural events, they do not tell the complete mechanistic story. To directly examine how running affects the brain, the researchers assessed changes to both neuronal and non-neuronal brain cells. Running induced widespread neuronal changes, including higher levels of pre- and postsynaptic markers throughout the brain (including in the hippocampus and orbitofrontal, medial prefrontal and perirhinal cortices), and increased density and length of dendritic spines in the medial prefrontal cortex. While these effects suggest that exercise elicits generalized synaptic changes, they do not explain why particular cognitive functions are selectively boosted over others.

The researchers therefore looked for this crucial link to behavior in astrocytes. As Brockett explains, “We hypothesized that all cells likely change as a function of experience. We chose to focus on astrocytes because there is lots of evidence suggesting that astrocytes could be implicated in cognitive behavior. Loss of astrocytes correlate with impairment on a cognitive task and astrocytes connect the majority of neurons to blood vessels. They extend numerous processes that envelop nearby synapses, and gliotransmitters have been implicated directly in LTP-induction.”

Confirming their suspicions, in runners, astrocytes increased in size (Figure, A-B) and showed more contacts with blood vessels (Figure, C-D). But these changes only occurred in the hippocampus, medial prefrontal cortex and orbitofrontal cortex – critically, all regions that support the tasks showing running-related improvement. In contrast, running did not alter astrocytes in the perirhinal cortex, a region necessary for novel object recognition, which did not benefit from running. Thus, while running modified both neurons and astrocytes, the pattern of selective cognitive enhancements corresponded only with changes to astrocytes.

In the hippocampus, medial prefrontal cortex and orbitofrontal cortex, astrocytes were larger and made more contacts with blood vessels for rats who ran than those who did not. Brockett et al., 2015

In the hippocampus, medial prefrontal cortex and orbitofrontal cortex, astrocytes were larger and made more contacts with blood vessels for rats who ran than those who did not. Brockett et al., 2015

Implications for the active human

Although the varied and widespread cognitive benefits of exercise have long been appreciated, this study provides some of the first insight into the remarkable selectivity of these enhancements. Follow-up studies will help elucidate why, from both biological and evolutionary perspectives, running would demonstrate such selectivity. Might, for example, attention or task-switching abilities have been more important than object recognition for the efficiency of both animals and our persistence-hunting, distance-runner ancestors? Does running more heavily recruit certain brain regions over others, making them more susceptible to remodeling?

Given the cognitive and neurobiological differences between rats and humans, future research will be important to help extrapolate beyond rodents. Currently it’s unclear how different forms of exercise enjoyed by humans – for instance, swimming, yoga or strength training – uniquely influence distinct cognitive functions. According to Brockett,

“There is a lot of evidence that running has numerous beneficial effects on rodent and human cognitive functioning, but it is likely that aerobic exercise in general is responsible for these effects rather than running per se.”

Perhaps most notably, these findings add to the growing pool of studies underscoring the importance of astrocytes in neural processes that support cognition, and reveal a novel role for these cells in experience-dependent plasticity. As Brockett explains:

“Astrocytes are a unique cell type that haven’t been explored as much as neurons by the field of Neuroscience at large. Few studies have directly examined the role of astrocytes in complex behavior, and this was our first attempt at investigating this question.”

References

Alaei H, et al (2007). Daily running promotes spatial learning and memory in rats. Pathophysiology. 14:105–8. doi:10.1016/j.pathophys.2007.07.001

Brockett AT, LaMarca EA, Gould E (2015) Physical Exercise Enhances Cognitive Flexibility as Well as Astrocytic and Synaptic Markers in the Medial Prefrontal Cortex. PLoS ONE 10(5): e0124859. doi:10.1371/journal.pone.0124859

Gibbs ME, O’Dowd BS, Hertz E, Hertz L (2006) Astrocytic energy metabolism consolidates memory in young chicks. Neuroscience 141(1): 9-13. doi:10.1016/j.neuroscience.2006.04.038

Henneberger C, Papouin T, Oliet SH, Rusakov DA (2010). Long-term potentiation depends on release of D-serine from astrocytes. Nature. 463:232-6. doi:10.1038/nature08673

Kramer AF, Erickson KI (2007). Capitalizing on cortical plasticity: influence of physical activity on cognition and brain function. Trends Cogn Sci. 11: 342–8. doi:10.1016/j.tics.2007.06.009

Marlatt MW, Potter MC, Lucassen PJ, van Praag H (2012). Running throughout middle-age improves memory function, hippocampal neurogenesis, and BDNF levels in female C57BL/6J mice. Dev Neurobiol. 72:943–52. doi:10.1002/dneu.22009

van Praag H, Kempermann G, Gage FH (1999). Running increases cell proliferation and neurogenesis in the adult mouse dentate gyrus. Nat Neurosci. 2:266–70. doi:10.1038/6368

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The Mitochondrial Hypothesis: Is Alzheimer’s a Metabolic Disease?

Originally published on the PLOS Neuroscience Community

Despite decades of research devoted to understanding its origins, Alzheimer’s disease remains a daunting and devastating neurological mystery, ranking as the sixth leading killer of Americans. Countless therapeutic attempts, each designed with fresh anticipation, have repeatedly failed. A common thread across many of these drugs is their targeting the defining marker of the disease, amyloid plaques – those nasty extracellular deposits of beta-amyloid protein that invariably present in the Alzheimer’s brain and are thought to be toxic to neurons. Given the frustrating loss of research money, time and effort, many scientists agree it’s time to stop running circles around the amyloid hypothesis and begin seriously considering alternative explanations. One such theory showing increasing promise is the “mitochondrial hypothesis”. Its proponents posit that mitochondrial dysfunction lies at the heart of neural degeneration, driven by metabolic abnormalities which lead to classic Alzheimer’s pathology.

Steps by which mitochondrial function may lead to Alzheimer's. Based on the model outlined in Swerdlow et al (2010).

Steps by which mitochondrial function may lead to Alzheimer’s. Based on the model outlined in Swerdlow et al (2010).

Thank mom for your genetic risk

The first hints at this possibility arose from epidemiological observations about the genetic patterns of Alzheimer’s prevalence. These findings suggest that genetic influences may include more nuanced interactions than the better-known contributions from genes such as ApoE and TOMM40. Although both parents determine genetic risk, your likelihood of getting Alzheimer’s is much higher if the affected parent was your mother. This argues strongly that some maternal element underlies the association. Mitochondrial DNA is a logical target, as this subset of DNA is solely passed down from the mother. Many features of Alzheimer’s show this same maternal-dominant inheritance; those whose mother (but not father) had the disease also show reduced glucose metabolism and cognitive function, as well as elevated PIB uptake (a marker of amyloid) and brain atrophy.

Are metabolic enzymes the pathological trigger?

So if mitochondrial dysfunction initiates the Alzheimer’s cascade, what are the steps leading from metabolic disruption to neurodegeneration and ultimately, dementia? Studies point to cytochrome oxidase – a key enzyme for mitochondrial metabolism that’s encoded by both mitochondrial and nuclear DNA – as a likely trigger for early pathological events. Studies suggest that the enzyme is dysfunctional in the earliest disease stages; its activity is reduced not just in those with Alzheimer’s, but even in asymptomatic individuals who are at genetic risk for the disease or had a mother with Alzheimer’s. Furthermore, this stunted activity is linked directly to mitochondrial (or maternal) genetic contributions. By simply replacing the mitochondrial portion of the cytochrome oxidase DNA with DNA from Alzheimer’s patients, an otherwise normal cell will now have reduced cytochrome oxidase activity.

Bridging metabolism to Alzheimer’s pathology

For the mitochondrial theory to hold water, it must critically account for the classic pathological markers that define Alzheimer’s and have shaped traditional disease models – namely, amyloid plaques, tau tangles and brain atrophy. Indeed, growing evidence is elegantly bridging altered mitochondrial function to these key markers. For instance, disrupting mitochondrial electron transport chain activity (if you’ve forgotten your basic biochemistry, this is essential to cell metabolism) increases phosphorylated tau. What’s more, inhibiting cytochrome oxidase promotes a host of neurotoxic downstream effects including increased oxidative stress, apoptosis and amyloid production. Conversely, there’s also evidence that amyloid disrupts electron transport chain and cytochrome oxidase function, posing a chicken-or-egg conundrum. Amyloid has been found to buddy-up to mitochondria, but which comes first, the amyloid or the mitochondrial dysfunction, isn’t entirely clear. Both events occur early in the disease process, even before individuals show any symptoms of cognitive impairment. Whatever the mechanism, neurons from Alzheimer’s patients show signs of increased mitochondrial degradation. And when a neuron’s “powerhouse” begins to degrade, it cannot possibly support normal cognitive function.

A promising path for progress

It remains to be seen whether metabolic dysfunction is the key to unlocking the mechanisms of Alzheimer’s, and to ultimately developing effective therapeutics. While the current evidence is quite promising, many of the issues underlying the failure of other theories (poor translation of animal findings to humans, the challenge of identifying causal mechanistic pathways, etc.) similarly apply to the mitochondrial hypothesis. But at the very least, the proposal lays new ground for neuroscientists to continue progressing forward after a recent history of frustrating dead-ends. Even if mitochondria don’t hold the answer researchers have been seeking, understanding its contributions to Alzheimer’s pathology can only bring us closer to solving the mystery of this devastating disease.

References

Crouch PJ et al (2005). Copper-dependent inhibition of human cytochrome c oxidase by a dimeric conformer of amyloid-beta1-42. J Neurosci. 25(3):672-9. doi: ​10.​1523/​JNEUROSCI.​4276-04.​2005

Debette S et al. (2009). Association of parental dementia with cognitive and brain MRI measures in middle-aged adults. Neurology. 73(24):2071-8. doi: 10.1212/WNL.0b013e3181c67833

Edland SD et al (1996). Increased risk of dementia in mothers of Alzheimer’s disease cases: evidence for maternal inheritance. Neurology. 47:254–6. doi: 10.​1212/​WNL.​47.​1.​254

Hirai K et al. (2001). Mitochondrial abnormalities in Alzheimer’s disease. J Neurosci. 21(9):3017-23

Hogliner GU et al. (2005). The mitochondrial complex I inhibitor rotenone triggers a cerebral tauopathy. J Neurochem. 95(4):930-9. doi: 10.1111/j.1471-4159.2005.03493

Honea RA et al. (2010). Reduced gray matter volume in normal adults with a maternal family history of Alzheimer disease. Neurology. 74(2):113-20. doi: 10.1212/WNL.0b013e3181c918cb

Kish SJ et al. (1992). Brain cytochrome oxidase in Alzheimer’s disease. J Neurochem. 59(2):776-9. doi: 10.1111/j.1471-4159.1992.tb09439

Mosconi L et al (2007). Maternal family history of Alzheimer’s disease predisposes to reduced brain glucose metabolism. Proc Natl Acad Sci. 104(48):19067-72. doi: 10.1073/pnas.0705036104

Mosconi L et al. (2010). Increased fibrillar amyloid-{beta} burden in normal individuals with a family history of late-onset Alzheimer’s. Proc Natl Acad Sci. 107(13):5949-54. doi: 10.1073/pnas.0914141107

Mosconi L et al. (2011). Reduced Mitochondria Cytochrome Oxidase Activity in Adult Children of Mothers with Alzheimer’s Disease. J Alzheimers Dis. 27(3): 483–490. doi: 10.3233/JAD-2011-110866

Roses AD et al (2010). A TOMM40 variable-length polymorphism predicts the age of late-onset Alzheimer’s disease. Pharmacogenomics J. 10(5): 375–84. doi: 10.1038/tpj.2009.69

Swerdlow RH et al. (1997). Cybrids in Alzheimer’s disease: a cellular model of the disease? Neurology. 49(4):918-25. doi: ​10.​1212/​WNL.​49.​4.​918

Swerdlow RH, Burns JM and Khan SM (2010). The Alzheimer’s Disease Mitochondrial Cascade Hypothesis. J Alzheimers Dis. 20 Suppl 2:S265-79. doi: 10.3233/JAD-2010-100339

Swerdlow RH. (2012). Mitochondria and cell bioenergetics: increasingly recognized components and a possible etiologic cause of Alzheimer’s disease. Antioxid Redox Signal. 16(12):1434-55. doi: 10.1089/ars.2011.4149

Valla J et al. (2010). Reduced posterior cingulate mitochondrial activity in expired young adult carriers of the APOE ε4 allele, the major late-onset Alzheimer’s susceptibility gene. J Alzheimers Dis. 22(1):307-13. doi: 10.3233/JAD-2010-100129

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Mapping Memory Circuits with High-Field FMRI

Originally posted on the PLOS Neuroscience Community

How we create and recall memories has long fascinated scientists, spurring decades of research into the brain mechanisms supporting memory. These studies overwhelmingly point to the hippocampus as an essential structure for memory formation; yet despite these efforts, we still don’t fully understand how hippocampal circuits transform stimulus input into stored memories, in part due to several fundamental methodological challenges.

The most commonly used functional imaging method in humans, fMRI, neither measures neural activity directly nor attains ideal spatiotemporal resolutions. Although more powerful, invasive techniques can be used in animals, it’s arguable whether they can be applied to assess higher cognitive functions like episodic memory, as the jury’s out on whether this process is uniquely human or shared with animals. However, recent neuroimaging advances are rapidly narrowing the power gap between invasive and non-invasive techniques, helping to reconcile findings across animal and human studies. In particular, high-field, high-resolution fMRI in humans is becoming more feasible, permitting sub-millimeter spatial resolution. Although the BOLD signal from fMRI only approximates the neural signal, such methodological advances get us one step closer to imaging neural activity during cognitive functions like memory formation. A team of researchers recently took advantage of high-field fMRI to investigate sub-region and layer-specific memory activity in the medial temporal lobe, an area critical for long-term memory acquisition.

The hippocampal-entorhinal circuit

Within the medial temporal lobe, the entorhinal cortex (EC) and hippocampus (including subfields dentate gyrus, CA1, CA2 and CA3) make up a well-characterized circuit, in which superficial EC layers project to the dentate gyrus and CA1 via the perforant path, on to CA3 via mossy fibers, to CA1 via schaffer collaterals, and finally return back to the deep layers of the EC. We know this circuit is important for memory, as the hippocampus is essential for memory encoding and other processes that presumably support memory, including novelty detection or pattern separation and completion. However, the mapping of these functions onto human entorhinal-hippocampal pathways is incomplete. 

The entorhinal-hippocampal circuit

The entorhinal-hippocampal circuit

Imaging memory with high-field fMRI

To examine how novelty and memory signals are distributed along the EC-hippocampus circuit, Maass and colleagues conducted high-resolution (0.8 mm isotropic voxels) 7T fMRI while participants performed an incidental encoding task. The subjects viewed a series of novel and familiar scenes during scanning, and later completed a surprise memory recall test on the scenes they had previously seen. This allowed the researchers to assess brain activity related to novelty – by comparing novel and familiar trials – as well as activity related to successful memory encoding – by comparing trials that were subsequently remembered and forgotten. On each subject’s structural brain image, they parcellated the EC into superficial input layers and deep output layers, and segmented the hippocampus into CA1 and a combined dentate gyrus/CA2/CA3 region (DG/CA2/3). 

Segmentation of entorhinal cortex layers (left) and hippocampal subfields (right). Maass et al., 2014.

Segmentation of entorhinal cortex layers (left) and hippocampal subfields (right). Maass et al., 2014.

Double-dissociation of novelty and encoding signals

Across participants, novel scenes activated DG/CA2/3, whereas successful encoding activated CA1, and the strength of this CA1 signal predicted retrieval accuracy. Next, Maass and colleagues looked at subject-level voxel-wise activity, which preserves high spatial resolution by eliminating the need for smoothing and across-subject averaging. Using multivariate Bayes decoding, which can be used to compare the log evidence that various regions predict a particular cognitive state, they evaluated whether EC or hippocampal regions predict novelty and memory encoding. As illustrated by the relative log evidences in the below graphs, DG/CA2/3 (A right) and CA1 (B right) respectively signaled novelty and encoding, consistent with their group-level findings. But this analysis further showed that superficial EC (the input layers to the hippocampus) and deep EC (the output layers from the hippocampus) also respectively predicted novelty (A left) and encoding (B left). What’s more, superficial EC and DG/CA2/3 functionally coupled during novelty processing, whereas deep EC and CA1 coupled during encoding. 

Multivariate Bayes decoding predicts novelty and encoding from entorhinal cortex and hippocampal activity. Maass et al., 2014.

Multivariate Bayes decoding predicts novelty and encoding from entorhinal cortex and hippocampal activity. Maass et al., 2014.

In essence, these findings suggest a division of labor across the EC-hippocampal circuit, where hippocampal input pathways participate in novelty detection, and output pathways transform these signals for memory storage. The researchers offer a model in which information about stimulus identity feeds in from upstream regions such as the perirhinal and parahippocampal cortices, which are known to process object and scene identity. Hippocampal pattern separation or comparator computations might then be performed to both assess novelty and reduce interference between stimulus representations, transforming the novelty signal into output for long-term storage. This explanation for how hippocampal circuits process a stimulus representation is reasonable, considering that DG/CA3 is important for pattern separation, and CA1 has been proposed as a neural comparator, processes which may determine the memory fate of a stimulus representation.

Cautions and caveats

A segregation of function across EC layers and hippocampal subfields does not necessarily imply that these mappings are mutually exclusive. For instance, it’s likely that output pathways still carry a novelty signal, and memory formation may begin earlier in the processing stream than detected here. Despite the impressive resolution in this study, allowing fine segmentation of cortical layers and subregions, noise and artifact are inherent concerns for any fMRI study. As the BOLD signal is a crude estimate of neural activity, there may well be a ceiling to the power of high-field fMRI, even with the most rigorous methods. How accurately these region- and layer-specific signals map onto memory functions therefore remains to be validated. And of course, we can’t infer directionality, causality or any direct relationship to neural activity from fMRI alone. It’s tempting to interpret early circuit activity as an input signal and late activity as an output signal, or to assume that the BOLD response reflects excitatory neural activity; however, we’ll need more direct neuroimaging tools to trace the flow of neural signal and confirm these speculations.

Together, Maass and colleagues’ study advances the field of cognitive neuroscience on two fronts. First, it helps bridge the gap between robust yet invasive imaging tools and non-invasive but less powerful approaches commonplace in human imaging studies. Their successful application of high-field fMRI demonstrates the feasibility of assessing human brain activity with sub-millimeter resolution, paving the way for the standardization and refinement of these tools. Second, and perhaps most critically, it allows us to peer into the brain at previously impossible scales to view the live hippocampal circuit hard at work, processing and engendering memories. While past fMRI studies have effectively shown where memories are woven together, these findings refine this anatomical precision to bring us one step closer to understanding how hippocampal circuits accomplish this feat.

First author Anne Maass kindly offered to answer a few questions about her research. Here is a brief interview with Maass and her colleagues.

Are there unique methodological concerns to consider when using high-field, high-resolution fMRI?

The increased signal-to-noise ratio provided by MRI at 7T enables us to acquire fMRI data at an unprecedented level of anatomical detail. However, ultra high-field fMRI is also more vulnerable to distortions and susceptibility-related artifacts and the negative effect of motion increases with resolution.

In particular, the anterior medial temporal lobe regions, such as the entorhinal cortex and perirhinal cortex, are often affected by susceptibility artifacts. Nevertheless, an optimized 7T protocol as we used in our study can reduce (but not fully eliminate) these signal dropouts and distortions, e.g. by the very small voxel size, shorter echo times as well as optimized shimming and distortion correction. We therefore had to manually discard functional volumes with visible dropouts and distortions.

The analysis of high-resolution functional data raises additional challenges, for instance the precise coregistration of structural and functional (often partial) images or the normalization into a standard space, which is usually done for group comparisons. In our study, we aimed to evaluate functional differences between entorhinal and hippocampal layers and subregions. We thus manually defined our regions of interest and chose a novel approach that enables to use the individual (raw) functional data to achieve highest anatomical precision.

Have other studies examined the hippocampal-entorhinal circuit during memory encoding or novelty detection using more direct neural imaging tools, for example, with intracranial EEG? If so, how do they align with your findings?

Although there have been several intracranial EEG recording studies in humans that investigated functional coupling between hippocampus and EC (i.e. Fernandez et al., 1999), to our knowledge, these studies have not been able to look at deep versus superficial EC or at specific hippocampal subfields.

Your findings have obvious implications for memory disorders. Have you done any work investigating how the hippocampal-entorhinal memory circuit is disrupted in Alzheimer’s or other dementias?

To investigate layer-specific processing in aging or neurodegenerative diseases is of course of particular interest as aging seems to affect particularly entorhinal input from superficial EC layers to the dentate gyrus and also taupathology in Alzheimer’s disease emerges in the superficial EC layers, subsequently spreading to particular hippocampal subregions or layers (i.e. CA1 apical layers). However, high-resolution fMRI at 7T is particularly challenging in older people. The high probability of exclusion criteria (e.g. implants) complicates subject recruitment and stronger subject movement increases motion artefacts. So far we have collected functional data at 7T in healthy older people with 1mm isotropic resolution that we are currently analyzing. In addition, further studies are planned that focus on changes in intrinsic functional connectivity of the hippocampal-entorhinal network in early Alzheimer’s disease.

What further questions do your results raise regarding hippocampal memory pathways, and do you have plans to follow-up on these questions with future studies?

One further question that we are currently addressing is how hippocampal and neocortical connectivity with the EC is functionally organized in humans.

While the rodent EC shows a functional division into lateral and medial parts based on differential anatomical connectivity with parahippocampal and hippocampal subregions, almost nothing is known about functional subdivisions of the human EC.

In addition to characterizing entorhinal functional connectivity profiles in young adults, we also want to study how these are altered by exercise training. Finally, we aim to resolve how aging affects object vs. scene processing (and pattern separation) in different components of the EC and subfields of the hippocampus.

References

Dere E et al. (2006). The case for episodic memory in animals. Neurosci Biobehav Rev 30(8):1206-24. doi:10.1016/j.neubiorev.2006.09.005

Fernandez G et al. (1999). Real-Time Tracking of Memory Formation in the Human Rhinal Cortex and Hippocampus. Science 285(5433):1582-5. doi:1 0.1126/science.285.5433.1582

Friston K et al. (2008). Bayesian decoding of brain images. Neuroimage 39(1):181-205. doi:10.1016/j.neuroimage.2007.08.013

Maass A, et al. (2014). Laminar activity in the hippocampus and entorhinal cortex related to novelty and episodic encoding. Nat Commun 5:5547. doi:10.1038/ncomms6547

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Brain Connectivity Patterns of Shifting Memory Processes

Originally published on the PLOS Neuroscience Community

At a recent dinner party, the memories flood your mind as you reminisce with an old friend. A woman approaches and your friend introduces you: “I’d like you to meet my wife, Margaret.” Your attention shifts from the past to this present moment, as you focus on making a new association between “Margaret” and the tall, dark-haired woman before you.

As during a dinner party with old friends and new acquaintances, the dynamically shifting stimulus landscape around us may trigger the retrieval of old memories or the formation of novel ones, often in overlap or rapid succession. What’s more, memory does not simply involve compartmentalized processes of the birth or reactivation of memories in isolation. Rather, successful execution of these processes also relies on support from non-mnemonic processing, such as evaluating a recalled memory or paying attention to new information. Although there is some overlap in the brain regions involved in laying down new memories and recovering old ones, the complex coordination of the many sub-processes of encoding and retrieval naturally requires cross-talk across distinct neural systems.

The brain’s medial temporal lobe is commonly considered the seat of memory – with the hippocampus lying at its heart – as encoding and retrieval rely critically on these regions. However, just as memory involves the coordination of many cognitive functions, so does it require the coordination of widespread brain networks. Both small-scale circuits across hippocampal subregions, and long-range brain systems, work together to integrate sensory information, control attention and filter relevant details in support of memory. A recent study from Katherine Duncan, Alexa Tompary, and Lila Davachi at NYU demonstrated just how the hippocampus shifts its communication with the surrounding brain to support its remarkable ability to rapidly switch between memory processes.

The researchers conducted fMRI while participants performed alternating blocks during which they encoded pairs of objects and then recalled those object pairs. A day later, participants returned for an unscanned long-term memory test, in which they reported whether they recognized the objects, and rated how confidently they recalled the pairs. This delayed memory test was used to measure how well the object associations had been encoded the day prior.

A standard analysis confirmed that across all hippocampal subregions (CA1, DG/CA3, subiculum) activity increased for both successful encoding and retrieval. Notably, the retrieval effect was strongest in DG/CA3, in line with past studies suggesting that this region might function as an auto-associative network that serves to reactivate stored memory traces. Now, we’ve long known that the hippocampus is engaged during these processes; but less certain is how the region interacts with the surrounding brain.

The researchers focused on hippocampal subregion CA1, an important hub along the bidirectional cortex-hippocampus highway, as it both receives input from the medial temporal lobe (via the dentate gyrus and CA3), and also provides output back to the cortex. Connectivity between DG/CA3 and CA1 was stronger during the retrieval than the encoding block, whereas connectivity with CA1 didn’t differ between memory blocks for any of the other medial temporal lobe or midbrain regions they investigated (Figure 1). Thus, not only was DG/CA3 highly activated, but it was also more strongly connected with its downstream hippocampal target, during retrieval.

Figure 1. Connectivity between CA1 and DG/CA3 is stronger during retrieval than encoding. Adapted from Duncan et al., 2014.

Figure 1. Connectivity between CA1 and DG/CA3 is stronger during retrieval than encoding. Adapted from Duncan et al., 2014.

But how might memory-specific communication across regions subserve the brain’s changing cognitive goals? To test whether connectivity patterns in fact support memory success, the researchers correlated functional connectivity measures with encoding and retrieval performance. Supporting their prior findings, CA1-DG/CA3 connectivity correlated with immediate retrieval accuracy, but not with long-term memory (i.e., day 2 retrieval) (Figure 2, left). Conversely, connectivity between CA1 and the ventral tegmentum correlated with long-term memory, but not immediate retrieval accuracy (Figure 2, right). As Davachi explains, “This suggests that whatever this signal represents, it is explaining long-term – not short-term – memory, which arguably suggests that across subject variability in CA1-ventral tegmentum connectivity is related to the consolidation of memories, not just their initial encoding.”

Figure 2. CA1-DG/CA3 connectivity correlates with immediate retrieval, whereas CA1-ventral tegmentum connectivity correlates with memory consolidation. Adapted from Duncan et al., 2014.

Figure 2. CA1-DG/CA3 connectivity correlates with immediate retrieval, whereas CA1-ventral tegmentum connectivity correlates with memory consolidation. Adapted from Duncan et al., 2014.

Notably, these connectivity patterns emerged when examining activity across each encoding or retrieval block, but disappeared when isolating the trial-evoked responses. It therefore seems possible that these increases in connectivity strength may not directly support isolated moments of memory formation or reactivation, but instead, auxiliary processes that evolve gradually over time. However, Davachi cautions “These null effects do not necessarily imply that there are not important trial-evoked changes in connectivity, but rather, that the trial-evoked data are simply swamped with the incoming perceptual and task signals.”

While the role of DG/CA3 and its connectivity to CA1 in associative retrieval has been well documented, the encoding-specific link between CA1 and the ventral tegmentum is less expected. Regions such as the medial temporal lobe and the prefrontal cortex are traditionally considered the major players in memory encoding; yet, recent research has hinted at a more important role for the ventral tegmentum than previously thought. Furthermore, this finding aligns well with animal studies showing that input to CA1 from the ventral tegmentum is required for synaptic plasticity, and that long-term potentiation – key to long-term memory formation – is dopamine-dependent. But as Davachi emphasizes, “You can never know if the BOLD response is related to long-term potentiation. All we show is that the coordinated activation between the ventral tegmentum and CA1 is related to successful encoding (and not retrieval) but what this represents is unclear.” Indeed, the ventral tegmentum is involved in a host of other, non-memory functions as well, such as novelty detection and motivation, both of which would be critical for the encoding task used here – or when making a new acquaintance at a dinner party. What remains to be determined is how, if at all, hippocampal connectivity with the ventral tegmentum supports memory consolidation, or rather, these adjunct processes that might be important for establishing new memories.

Although this study demonstrated unique hippocampal interactions during encoding and retrieval, it can’t speak to the direction of information flow. For instance, since the hippocampal-ventral tegmentum connection is reciprocal, signaling could feasibly proceed in either direction. Furthermore, their findings don’t show that encoding and retrieval are exclusively associated with CA1-ventral tegmentum and CA1-DG/CA3 connectivity, respectively – only that the strength of these interactions differs depending on the memory manipulation.

While further studies, especially those which more directly measure neural activity, will help clarify questions concerning directionality and causality, these findings build significantly upon our knowledge of human memory. In particular, Duncan and colleagues’ techniques enable the assessment of communication with and across hippocampal subregions while directly evaluating memory, which has been challenging in animals. Their findings not only raise several important questions for follow-up, but critically, also bridge the gap between human and animal studies to help unify our understanding of the brain systems supporting encoding and retrieval.

References

Duncan K, Tompary A and Davachi L (2014). Associative encoding and retrieval are predicted by functional connectivity in distinct hippocampal area CA1 pathways. J Neurosci 34(34): 11188-98. doi: 10.1523/jneurosci.0521-14.2014

Murty VP and Adcock RA (2014). Enriched encoding: Reward motivation organizes cortical networks for hippocampal detection of unexpected events. Cereb Cortex 24(8):2160-8. doi: 10.1093/cercor/bht063

Treves A and Rolls ET (2004). Computational constraints suggest the need for two distinct input systems to the hippocampal CA3 network. Hippocampus 2(2):189-99. doi: 10.1002/hipo.450020209

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Global similarity signals of recognition strength

The below article was recently rejected from the Journal of Neuroscience as a ‘Journal Club’ commentary on Davis et al., 2014, ‘Global neural pattern similarity as a common basis for categorization and recognition memory’. Hoping that my efforts will not go to waste, I’d like to give the piece an alternate home here. Please read, comment and share, all free of paywalls!

Theories of long-term memory have linked an item’s memory strength to its “global similarity” (Clark and Gronlund, 1996). The greater the conceptual overlap between a target item and other items stored in memory, the more familiar the item will seem. While psychological models have consistently supported the theory that across-item similarity contributes to recognition memory, it is unclear how neural computations give rise to this relationship between representational similarity and memory strength. Neuroimaging studies have shown that activity in the brain’s medial temporal lobe tracks memory strength as well as the representational overlap between stimuli in memory, establishing this region as a likely host for a global similarity signal that confers accuracy and confidence to recognition judgments.

Model of the multivoxel pattern similarity analysis. A) The multivoxel activation pattern within a region is extracted for each stimulus (S1, S2, etc.). B) The correlation between the activation pattern for each stimulus and that of all other stimuli is computed. Across-item correlations are expected to be higher for stimuli that are strongly remembered than those that are poorly remembered. Adapted from Xue et al., 2010.

Model of the multivoxel pattern similarity analysis. A) The multivoxel activation pattern within a region is extracted for each stimulus (S1, S2, etc.). B) The correlation between the activation pattern for each stimulus and that of all other stimuli is computed. Across-item correlations are expected to be higher for stimuli that are strongly remembered than those that are poorly remembered. Adapted from Xue et al., 2010.

In their study recently published in the Journal of Neuroscience, Davis and colleagues (2014) tested whether the similarity between blood oxygen level dependent (BOLD) activation patterns elicited by an item and other encoded items predicted how confidently the item would later be recognized (see figure). Participants underwent functional magnetic resonance imaging while performing an incidental encoding task of repeated Chinese words. They were later administered a surprise recall task, in which they freely reported any recalled words from the encoding task, as well as a surprise recognition task, in which they reported their confidence in having previously seen a word. The authors then computed a neural similarity score for each word, which measured the correlation between the multivoxel activity pattern for that word and the activity pattern for all other words (Davis et al., Figure 1). This global similarity metric was compared to recognition confidence ratings to assess the relationship between neural representational overlap and memory strength.

Consistent with their prediction, the extent of global similarity between the multivoxel activation pattern of a word and that of all other words correlated with the word’s subsequent recognition confidence ratings (Davis et al., Figure 3A). Within their medial temporal lobe region of interest, the effect was localized to clusters in both the parahippocampal gyrus and hippocampus. This link between neural global similarity and recognition strength held even after controlling for within-item similarity, which the authors previously showed to correlate with memory strength (Xue et al., 2010). Furthermore, medial temporal lobe pattern similarity also correlated with the semantic relatedness between words (Davis et al., Figure 6). This demonstrated, for the first time, that medial temporal lobe substrates of between-item similarity mirror psychological metrics of memory strength and semantic relatedness. The stronger an item is represented in memory, the more highly its semantic content, and its representation in the medial temporal lobe, overlaps with that of other stimuli.

While these initial results speak to the mechanisms by which an item is perceived as familiar, considerable debate exists over whether recognition is mediated by a single neural system. For instance, some neuroimaging and lesion studies have reported functional segregation of familiarity and recollection signals within the medial temporal lobe (Eichenbaum et al., 2007), while others indicate that the medial temporal lobe collectively supports all forms of recognition memory (Squire et al., 2007). Furthermore, there is evidence that successful recall can be mediated by global similarity (Gillund and Shiffrin, 1984), but also by pattern separation of an item from other items (O’Reilly and Norman, 2002). Thus, to dissociate between effects of global similarity on recognition strength and on recall, pattern similarity analyses were additionally performed on non-recalled words alone, and also on recalled versus non-recalled words. Global similarity of medial temporal lobe activation patterns both correlated with the memory strength of non-recalled words and predicted recall success. Thus, both recognition and recall may rely on the degree of neural representational overlap across items in memory.

Finally, the authors investigated whether the association between memory strength and neural similarity extends beyond long-term memory. Given prior evidence that learning categorical rules increases the psychological similarity of learned items and activates the medial temporal lobe, they tested whether global similarity of medial temporal lobe representations reflected category learning. Indeed, global similarity of medial temporal lobe activity patterns correlated with the psychological similarity between an item and others in its category (Davis et al., Figure 3B, C). Notably, these regions overlapped considerably with those from the long-term memory pattern similarity analysis (Davis et al., Figure 4). Thus, global similarity computations in the medial temporal lobe may not selectively subserve episodic memory formation, but might support a range of learning processes.
These findings suggest a universal mechanism of coding memory strength within the medial temporal lobe that generalizes across domains, beyond just recognition memory. This bridge across cognitive domains aligns well with our understanding that acquiring both episodic memories and categorization rules involves learning new information, a process fundamentally supported by memory encoding. However, the breadth of such a medial temporal lobe code, which extends beyond recognition to encompass recall and categorization, raises important considerations regarding the heterogeneity versus homogeneity of medial temporal lobe memory functions.

Some theories of medial temporal lobe function propose distinct roles for the hippocampus and parahippocampal gyrus in long-term memory. A recent study examined whether these regions also functionally dissociate according to memory-related global similarity computations. LaRocque et al. (2013) reported a correlation between across-item neural similarity and recognition strength in the parahippocampal gyrus, but an inverse correlation in the hippocampus. This dissociation contrasts with the parallel representations in the hippocampus and parahippocampal gyrus observed by Davis et al. (2014). Thus, hippocampal representations of global similarity and distinctiveness may both contribute to recognition memory. These seemingly contradictory findings in fact align with computational models of hippocampal function suggesting that the structure performs both pattern completion and separation in the service of long-term memory (Yassa and Stark, 2011). These operations are likely computed by separate hippocampal subregions and support distinct memory functions. Specifically, pattern separation may be mediated by the dentate gyrus and promote discriminative processes that aid encoding and recollection, whereas pattern completion may be mediated by CA3 and generalize across inputs to signal familiarity. It is therefore possible that hippocampal signals of representational overlap and distinctiveness coexist in complex tasks like those employed in these studies (LaRocque et al., 2013; Davis et al., 2014), which may dynamically engage concurrent memory encoding and retrieval processes. Follow-up studies will help to resolve why a pattern completion or separation signal would dominate depending on the task condition or memory manipulation.

Furthermore, given the inherent ambiguity of multivoxel signal content, it is unclear what particular information is carried in overlapping activation patters. Here, BOLD patterns correlated with both memory strength and semantic content; yet, multiple additional variables may covary with these cognitive measures and hence contribute to the similarity across multivoxel space. As the authors acknowledge, an infinite number of factors, which can be challenging to detect or control, may increase the similarity between BOLD activation patterns (Todd et al., 2013). Further research will be important to more completely characterize how variance in factors such as stimulus features, cognitive sub-processes, BOLD dynamics, or analysis procedures, may additionally drive the overlap in BOLD patterns of neural representations.

The findings of Davis and colleagues provide novel insight into medial temporal lobe coding mechanisms of memory strength, linking computational models that implicate psychological similarity in recognition strength with representational similarity of memory-related brain activation patterns. Together, these results solidify a base upon which to more thoroughly examine the breadth of this medial temporal lobe similarity signal across cognitive processes. Such findings will serve as critical steps towards clarifying the extent to which overlapping neural representations in the hippocampus and parahippocampal gyrus contribute to a range of learning processes – including both those within and beyond the domain of episodic memory.

References

1. Clark SE, Gronlund SD. 1996. Global matching models of recognition memory: How the models match the data. Psychon Bull Rev 3:37-60.
2. Davis T, Xue G, Love BC, Preston AR, Poldrack RA. 2014. Global neural pattern similarity as a common basis for categorization and recognition memory. J Neurosci 34:7472-84.
3. Eichenbaum H, Yonelinas AP, Ranganath C. 2007. The medial temporal lobe and recognition memory. Annu Rev Neurosci 30:123-52.
4. Gillund G, Shiffrin RM. 1984. A retrieval model for both recognition and recall. Psychol Rev 91:1-67.
5. LaRocque KF, Smith ME, Carr VA, Witthoft N, Grill-Spector K, Wagner AD. 2013. Global similarity and pattern separation in the human medial temporal lobe predict subsequent memory. J Neurosci 33:5466-74.
6. O’Reilly RC, Norman KA. 2002. Hippocampal and neocortical contributions to memory: advances in the complementary learning systems framework. Trends Cogn Sci 6:505-10.
7. Squire LR, Wixted JT, Clark RE. 2007. Recognition memory and the medial temporal lobe: a new perspective. Nat Rev Neurosci 8:872-83.
8. Todd MT, Nystrom LE, Cohen JD. 2013. Confounds in multivariate pattern analysis: Theory and rule representation case study. Neuroimage 77:157-65.
9. Xue G, Dong Q, Chen C, Lu Z, Mumford JA, Poldrack RA. 2010. Greater neural pattern similarity across repetitions is associated with better memory. Science 330:97-101.
10. Yassa MA, Stark CE. 2011. Pattern separation in the hippocampus. Trends Neurosci. 34:515-25.

ResearchBlogging.org
Davis T, Xue G, Love BC, Preston AR, & Poldrack RA (2014). Global neural pattern similarity as a common basis for categorization and recognition memory. The Journal of neuroscience : the official journal of the Society for Neuroscience, 34 (22), 7472-84 PMID: 24872552

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A BOLD signal in the hippocampus ambiguous

When you see a red blob on an fMRI activity map, what do you think? We all know fMRI doesn’t directly measure neural activity, yet an increased BOLD (blood oxygen level dependent) response is commonly used as a proxy for elevated “brain activity”. This interpretation is, in fact, strongly supported by studies identifying a relationship between the BOLD response and underlying neural activity. In particular, this signal correlates most strongly with the LFP (local field potential), suggesting that synaptic potentials – rather than spiking – primarily drive the BOLD signal 1.

But what about those blue blobs on that brain map? What exactly does a negative BOLD response represent? Do BOLD signal increases and decreases respectively represent neural activation and deactivation, as we often presume? Neuroscientists know the story isn’t that simple, yet still, we often construct our interpretations according to such idyllic principles.

fMRI 101

BOLD

In fMRI 101 we learned that the BOLD response results from changes in the relative amounts of oxygenated and deoxygenated hemoglobin, which – because of their distinct magnetic properties – are respectively associated with BOLD signal increases and decreases. When a brain region becomes active and requires energy, oxygen metabolism (CMRO2) increases, reducing blood oxygenation levels. A coincident increase in cerebral blood flow (CBF) partially counteracts this by delivering more oxygenated blood to the area. Since the BOLD signal increases with higher blood oxygenation, the direction of the BOLD response depends on the relative change in CBF and CMRO2. Since the increase in oxygenated blood flow typically exceeds that of oxygen metabolism, elevated neural activity (usually) leads to a positive BOLD response. So if a positive fMRI activation reflects increased blood blow and metabolism, negative activity should reflect the opposite … right?

Hippocampus ambiguous

One oft-overlooked feature of this mechanism is that the coupling between blood flow and metabolism varies across brain regions. Across the cortex, the coupling ratio between CBF and CMRO2 is heterogeneous but generally high, on the order of 2 to 4.5 2,3, generating a reliably positive BOLD signal with activation. But recent studies have shown that other regions have lower coupling ratios. Of particular concern is the hippocampus, with an estimated coupling ratio of 1.7 4. One possible reason for this discrepancy is the remarkably poor vascular supply to the hippocampus compared to the surrounding cortex 5. Thus, hippocampal activation would result in a notably reduced BOLD response compared to a cortical activation. As this CBF:CMRO2 coupling ratio flirts dangerously with unity, it raises concern that in certain situations it might dip to or below one, resulting in no change, or even a negative BOLD response, following neural activation. Indeed, upon stimulating neural activity (by inducing seizures) in rats, researchers observed a positive BOLD signal in the cortex, but a negative signal in the hippocampus 6.

What’s a hippocampal imager to do?

So what does all this mean for us foolish – I mean, unfortunate – cognitive neuroscientists using fMRI to study the hippocampus? For one, we face vastly greater challenges to interpreting our data than our lucky cortical colleagues. When the hippocampus activates, we can be relatively confident that blood flow and metabolism (and presumably, underlying neural activity) are concurrently elevated. But a deactivated hippocampus is an ambiguous hippocampus. A negative BOLD response could theoretically indicate an underlying decrease or increase in both or either parameters. Let’s explore three alternative scenarios which could theoretically engender a negative hippocampal BOLD signal.

1. ↓ CBF, ↓ CMRO2. The most intuitive explanation is that neural activity declines, reducing both blood flow and oxygen metabolism within the region. This scenario is certainly feasible if the hippocampus maintains a certain level of tonic activity and a given condition actively suppresses it below baseline.

2.  CBF, = CMRO2. Since the ratio of CBF to CMRO2 is the key determinant of the BOLD response, a change in oxygen metabolism is not requisite for a negative BOLD signal if blood flow alone declines. Such is the premise for the “vascular steal” hypothesis, which posits that blood is diverted from less critical regions to those directly involved in the task at hand, regardless of any change in oxygen consumption.

3.  CBF,  CMRO2. While the former two scenarios imply reduced hippocampal recruitment, either metabolic or vascular, a final scenario entails the opposite: elevated blood flow and metabolism drive the negative BOLD. Because of the hippocampus’ problematic coupling ratio, if the metabolic increase exceeds the blood flow increase, this manifests as a negative response.

Alternative interpretations

To disambiguate these alternatives, we must think outside the blob and interpret our effects in light of integrated electrophysiology, lesion and cognitive psychology findings. Two examples from recent fMRI studies illustrate the aforementioned challenges as well as how alternative explanations best account for a task-induced hippocampal deactivation.

First (shameless self-promotion alert!), during effortful memory retrieval, we consistently observe a negative hippocampal response 7-9. What might this signal represent? Given that the hippocampus is critically involved in encoding new memories 10, it’s possible that it remains continuously “online”, storing features of our ongoing experience into memory. Now, when one engages in a difficult mental task, such as trying to recall a weak memory, attention is diverted away from encoding irrelevant background information towards the target task. Scenario one would nicely account for this observation, as hippocampal neural activity dips below its baseline level and generates a negative BOLD. Considering that this negative response correlates with task difficulty (indexed by either response times or memory strength) and impaired encoding of the background environment, this seems like the most logical scenario. For now, that’s our story and we’re sticking with it (but please get in touch if you have other ideas!)

Yet in other situations, negative hippocampal responses have been observed during conditions in which, based on lesion and electrophysiological studies, one might expect the hippocampus to activate. For instance, a recent study observed hippocampal deactivation during landmark-based spatial memory retrieval 11. In this case, as the authors propose, the task-induced deactivation just might reflect neural activation.

Of course, we can’t simply choose a preferred explanation at whim that best supports our hypothesis. Au contraire, carefully considering the complicated nature of the hippocampal BOLD response might help expand our too-often blob-centric minds, and set a framework from some pretty awesome multi-modal hypothesis testing. Science isn’t supposed to be easy, but it can still be fun. Now, who else is eager to go crazy with some hippocampal calibrated fMRI and depth recordings?

References

1. Logothetis NK & Wandell BA. 2004. Interpreting the BOLD signal. Annu Rev Physiol 66:735-69.
2. Hoge RD et al. 1999. Linear coupling between cerebral blood flow and oxygen consumption in activated human cortex. Proc Natl Acad Sci U S A 96:9403-8.
3. Leontiev O et al. 2007. CBF/CMRO2 coupling measured with calibrated BOLD fMRI: sources of bias. Neuroimage. 36:1110-22.
4. Restom K et al. 2008. Calibrated fMRI in the medial temporal lobe during a memory-encoding task. Neuroimage. 40:1495-1502.
5. Borowsky IW & Collins RC. 1989. Metabolic anatomy of brain: a comparison of regional capillary density, glucose metabolism, and enzyme activities. J Comp Neurol. 288:401-13.
6. Schridde U et al. 2008. Negative BOLD with large increases in neuronal activity. Cereb Cortex. 18:1814-27.
7. Reas ET & Brewer JB. 2013a. Effortful retrieval reduces hippocampal activity and impairs incidental encoding. Hippocampus. 23:367-79.
8. Reas ET & Brewer JB. 2013b. Imbalance of incidental encoding across tasks: An explanation for non-memory-related hippocampal activations? J Exp Psych-Gen. 142:1171-9.
9. Reas ET et al. 2011. Search-related suppression of hippocampus and default network activity during associative memory retrieval. Front Hum Neurosci. 5:112.
10. Squire LR et al. 2004. The medial temporal lobe. Annu Rev Neurosci. 27:279-306.
11. Nilsson J et al. 2013. Negative BOLD response in the hippocampus during short-term spatial memory retrieval. J Cogn Neurosci. 25:1358-71.

ResearchBlogging.org
Reas ET, & Brewer JB (2013). Imbalance of incidental encoding across tasks: An explanation for non-memory-related hippocampal activations? Journal of experimental psychology. General, 142 (4), 1171-9 PMID: 23773160

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To the depressed brain, it’s all the same

If you’re among the 8-12% of the population who will suffer from depression during their lifetime 1, you’re painfully aware of its debilitating symptoms – hopelessness, indifference, emptiness. While it’s clear how depression radically affects one’s emotional state, emerging research is showing that the reaches of depression extend far beyond our mood. In fact, depression can disrupt basic cognitive functions, with a particularly devastating impact on memory 2. However, at present we don’t fully understand the brain processes that give rise to depression, never mind how they contribute to the disorder’s associated cognitive impairments. A recent study published by neuroscientists at Brigham Young University suggests that depression is associated with problems with a particular memory operation known as pattern separation.

Can you tell Buddy from Fuzzy? Thank your hippocampus

Yassa & Stark, 2011

Imagine your two neighbors both have black labs, one slightly smaller and fuzzier than the other. A part of your brain called the hippocampus registers the subtle differences between the dogs and creates distinct representations of the two. You recognize Buddy and Fuzzy as unique because your brain effectively pattern separated them, forming distinct memories of each. It is this process that the researchers speculated might go awry in depression.

Earlier studies have shown that depressed individuals have smaller 4 and less active 5 hippocampi during memory formation than non-depressed people. Although pattern separation is not the only memory function mediated by the hippocampus, depression symptoms can manifest as a tendency to overgeneralize, the opposite of pattern separating. The authors therefore wondered whether diminished pattern separation might lie at the heart of depression-related memory problems.

Pattern separation to the test

To test their hypothesis, they had 98 adults perform a memory test that demanded pattern separation, and complete questionnaires evaluating depression, anxiety, sleep and exercise. Participants with higher depression scores performed significantly worse on the memory test than those with low depression scores, consistent with past studies. Critically, the depressed group had particular difficulty distinguishing a new item they had never seen from a similar one they previously encountered, indicating that they were pattern separating poorly. Furthermore, they found that the higher an individual’s depression score, the worse their pattern separation performance. Importantly, there was no correlation between pattern separation and anxiety, sleep or exercise, suggesting that the memory deficit was specifically related to depression, and not confounded by other associated factors.

But why don’t depressed brains pattern separate?

This study goes a step beyond prior work to identify what specific memory function is compromised in depression. Although the study didn’t examine the neurobiological processes underlying the pattern separation deficit, its findings provide a clear direction for further research.

Past studies suggest that newly born neurons in the hippocampus, produced by neurogenesis, contribute to pattern separation 6 and an association between reduced neurogenesis and “depression” in animals 7. If there are links between pattern separation and neurogenesis, neurogenesis and depression, and now depression and pattern separation, might a causal relationship exist among the three? The authors propose that depression could inhibit neurogenesis, thus impairing pattern separation. Alternatively, reduced neurogenesis (which can be regulated by numerous factors such as exercise, drugs or a rich environment) may induce depressive symptoms. Given the invasive nature of currently available methods to study neurogenesis, examining the effect of neurogenesis on human depression and memory is no easy feat (although some innovative folks recently devised a clever way to document human neurogenesis).

Can poor pattern separation make you sad?

But this study raises another, possibly more accessible, question over how pattern separation is involved in emotional regulation. Whereas the average person might readily discriminate between similar objects or experiences, someone suffering from depression would emphasize the similarities. A pathological tendency to excessively generalize could account for an unwarranted negative outlook.

“That last party was so awkward, I should just stop trying to be social.”

“I was bad at my last job, so I’ll certainly fail at any job I try”.

We’ll have to wait on future research to fully understand whether poor pattern separation contributes to a negative outlook, as well as the brain basis of memory impairments in depression. In the meantime, take a moment to notice the subtle differences around you – it just might make you happy.

References

1. Andrade L et al. 2003. The epidemiology of major depressive episodes: results from the International Consortium of Psychiatric Epidemiology (ICPE) Surveys. Int J Methods Psychiatr Res. 12:3-21.
2. Zakzanis KK et al. 1998. On the nature and pattern of neurocognitive function in major depressive disorder. Neuropsychiatry Neuropsychol Behav Neurol. 11:111-9.
3. Shelton DJ & Kirwan CB. 2013. A possible negative influence of depression on the ability to overcome memory interference. Behav Brain Research.
4. Videbech P & Ravnkilde B. 2004. Hippocampal volume and depression: A meta-analysis of MRI studies. Am J Psychiatry. 161:1957-66.
5. Fairhall SL et al. 2010. Memory related dysregulation of hippocampal function in major depressive disorder. Biol Psychol. 85:499-503.
6. Clelland CD et al. 2009. A functional role for adult hippocampal neurogenesis in spatial pattern separation. Science. 325:210-3.
7. Petrik D et al. 2012. The neurogenesis hypothesis of affective and anxiety disorders: are we mistaking the scaffolding for the building? Neuropharmacology. 62:21-34.

ResearchBlogging.org
Shelton DJ & Kirwan CB (2013). A possible negative influence of depression on the ability to overcome memory interference Behavioral Brain Research DOI: 10.1016/j.bbr.2013.08.016

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The hippocampus: Going the distance beyond space and time

Anyone who’s spent a day exploring the streets of New York City will understand the importance of integrating accurate representations of space, time and distance. That hipster dive-bar you’re dying to check out is twelve streets from tonight’s dinner spot, whereas that hot new rooftop bar is just four avenues away. Which do you choose to save the most time and energy? A tiring day of shopping has taught you that a Manhattan block is not just a block; those four avenues are in fact equidistant to the twelve streets. But distance and time don’t always perfectly correlate; the tourists and congestion near the rooftop bar will certainly cost you a few minutes … dive-bar it is.

Place cells. Dots represent hippocampal spiking, color coded by neuron. Each cell has a preferred place field.

Place cells. Dots represent hippocampal spiking, color coded by neuron. Each cell has a preferred place field.

How do our brains represent and bind together the temporal and spatial features of our experiences? Since the discovery of place cells in the 1970’s we’ve known that the hippocampus is important for signaling where an animal is in its environment (O’Keefe, 1976). These hippocampal neurons possess distinct place fields, regions of space in which the cell preferentially fires with maximum frequency.

Neuroscientists have recently discovered that the hippocampus not only codes spatial information, but also represents information about when particular events occur. These neurons, aptly named “time cells”, selectively fire during particular moments of an experience. For example, when a rat runs on a treadmill, neuron A might fire one minute into its jog, whereas neuron B will remain silent until ten minutes in. But in such paradigms, time and distance are correlated, such that the longer a rat runs, the further he’s gone. This confound between time and distance makes it difficult to know whether a purported time cell indeed codes temporal information, or instead might be tuned to distance, or the integration of both spatial and temporal features.

Benjamin Kraus and colleagues set out to disentangle these confounding effects of time and distance in their study recently published in Neuron (Kraus et al., 2013). As in prior experiments, they trained rats to run on a treadmill while they recorded from hippocampal pyramidal cells, the region’s primary excitatory neurons. Many of the neurons (43%) were active when the rats ran, with most showing time cell-like behavior, demonstrating peak firing at particular moments during the run.

The activity of time cells (A and B) is locked to time running, and the acitivty of distance cells (C and D) is tuned to distance run.

The activity of time cells (A and B) is locked to time running, and the activity of distance cells (C and D) is tuned to distance run.
From Kraus et al., 2013.

Critically, to dissociate spiking locked to time versus distance they next varied the rats’ running speed, and examined hippocampal activity referenced either to running time or running distance. Thus, a neuron that consistently fired after five minutes of running, regardless of distance covered, would be labeled a time cell. Likewise, a neuron that fired after running two meters would be considered a distance cell. They found cells of both types, indicating that the hippocampus not only represents elapsed time, but also distance covered. Next, they measured how much space, time or distance influenced a given cell’s response. Although they found a small portion of cells that were only tuned to time or distance, most wore many hats, simultaneously performing the roles of time cell, distance cell and place cell.

So maybe the hippocampus isn’t simply a neural map, clock or ruler, but serves more as the brain’s GPS. Its integrated spatial and temporal code could serve the obvious function of helping us navigate the space-time continuum in which our world is embedded. But does its role end there, or might it serve a higher purpose? The hippocampus was once thought to have a Jekyll-and-Hyde personality, with alter-egos subserving both spatial navigation and memory for past experiences. But scientists are now unraveling how these distinct roles interactively support one another. Episodic memories aren’t one-dimensional snapshots, but rather, integrate details about events with the spatial and temporal context in which they’re experienced. While there’s strong evidence that the hippocampus subserves memory by binding such contextual features together, the neural computations by which it accomplishes this feat are unclear. Kraus et al’s findings contribute a critical piece to this puzzle, identifying hippocampal neurons that simultaneously represent the “what”, “where” and “when” of an experience, whose coordinated activity just might hold the code for a rich, multi-dimensional memory landscape.

References

Kraus BJ et al. 2013. Hippocampal “time cells”: Time versus path integration. Neuron. 78:1090-101.

O’Keefe J. 1976. Place units in the hippocampus of the freely moving rat. Exp Neurol. 51:78-109

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ResearchBlogging.org
Kraus BJ, Robinson RJ 2nd, White JA, Eichenbaum H, & Hasselmo ME (2013). Hippocampal “Time Cells”: Time versus Path Integration. Neuron, 78 (6), 1090-101 PMID: 23707613

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