Tag Archives: neuroimaging

How reliable is resting state fMRI?

Originally published on the PLOS Neuroscience Community

Arguably, no advance has revolutionized neuroscience as much as the invention of functional magnetic resonance imaging (fMRI). Since its appearance in the early 1990’s, its popularity has surged; a PubMed search returns nearly 30,000 publications with the term “fMRI” since its first mention in 1993, including 4,404 last year alone. Still today, fMRI stands as one of the best available methods to noninvasively image activity in the living brain with exceptional spatiotemporal resolution. But the quality of any research tool depends foremost on its ability to produce results in a predictable and reasonable way. Despite its widespread use, and general acceptance its efficacy and power, neuroscientists have had to interpret fMRI results with a large dose of partially-blind faith, given our incomplete grasp of its physiological origins and reliability. In a monumental step towards validation of fMRI, in their new PLOS One study Ann Choe and colleagues evaluated the reproducibility of resting-state fMRI in weekly scans of the same individual over the course of 3.5 years.

One devoted brain

Although previous studies have reported high reproducibility of fMRI outcomes within individuals, they’ve compared only few sessions over brief periods of weeks to months. Dr. Choe and her team instead set out to thoroughly characterize resting state brain activity at an unprecedented time scale. To track patterns of the fMRI signal, one dedicated 40 year-old male offered his brain for regular resting-state fMRI sessions. Over the course of 185 weeks, he participated in 158 scans, roughly occurring on the same day of the week and time of day. For comparison – just in case this particular individual’s brain was not representative of the general population – a group of 20 other participants (22-61 years old) from a prior study were used as reference.

Reproducibility of brain networks and BOLD fluctuations

The researchers identified 14 unique resting state brain networks. Networks derived from the subject’s individual scans were spatially quite similar to those identified from that subject’s average network map and the multi-subject average map, and these network similarity measures were highly reproducible. Whereas executive function networks were the most reproducible, visual and sensorimotor networks were least. The relatively low reproducibility of “externally directed” networks could be attributable to the nature of the unrestrained scanning conditions, in which mind-wandering or undirected thoughts could engage an array of sensory experiences. Dr. Choe suspects “that under truly controlled conditions, exteroceptive networks would become more reproducible. Differences in reproducibility in exteroceptive versus interoceptive networks should be seen as an observation that requires follow up study.”

Figure 1. Spatial similarity of weekly fMRI sessions for sensorimotor, visual and executive networks. (Choe et al., 2015)

Figure 1. Spatial similarity of weekly fMRI sessions for sensorimotor, visual and executive networks. (Choe et al., 2015)

The basic signal underlying fMRI is the blood oxygen level dependent (BOLD) response, a measure of changes in blood flow and oxygenation thought to reflect vascular and metabolic responses to neural activity. The magnitudes of BOLD fluctuations were similar both across the single subject’s scans and the group’s scans, although these fluctuations were generally more reliable within-subject. Similar to the spatial overlap between networks, BOLD signal in executive networks was most reproducible, while that in default mode and sensorimotor networks were least reproducible across the subject’s sessions.

Between-network connectivity

In the brain, no network is an island, but rather, is in constant communication with other regions, near and far. This functional connectivity can be assessed with fMRI by computing correlations in the signal between areas. As might be expected, connectivity was highest between networks involved in related functions, for example between sensorimotor and auditory networks, and between sensorimotor and visual networks. Connectivity between networks was similar in the single subject and multi-subject datasets, and was highly reproducible both across the single subject’s sessions and within the multi-subject dataset.

Figure 2. Between network connectivity for single-subject and multi-subject datasets. (Choe et al., 2015)

Figure 2. Between network connectivity for single-subject and multi-subject datasets. (Choe et al., 2015)

fMRI over the years

A unique advantage of their study design was the rich temporal information provided from repeated scanning over a multi-year period. This allowed them to not only assess the reproducibility of the BOLD signal, but also to explore trends in how it may change with the passage of years or seasonal fluctuations. Significant temporal trends were found in spatial similarity for the majority (11 of 14) of networks, in BOLD fluctuations for two networks, and in between-network connectivity for many (29 of 105) network pairs. All but one of these trends were positive, indicating increased stability of the fMRI signal over time. What drives these changes over the years isn’t entirely clear. It could simply reflect habituation to the scanning environment, for example, if the experience becomes increasingly repetitive and familiar with exposure. Alternatively, the authors suggest, it might involve physiological changes to the aging brain, such as synaptic or neuronal pruning. Over the 3.5-year study, the 40-year old participant indeed showed decline in his gray matter volume; this neural reorganization could feasibly impact the stability of the fMRI signal. However, Dr Choe cautions that “although three years is a long time, it is certainly not long enough to address the issue of say, an aging brain.”

Notably, many networks showed annual periodicity in their spatial similarity (9 of 14 networks) and BOLD fluctuations (3 networks). These measures also correlated with the local temperature, linking reliability of the fMRI signal with seasonal patterns. Although speculative, the authors suggest that this may in part relate to circadian or other homeostatic rhythms that regulate brain activity. Dr. Choe and her group “were surprised to discover annual periodicity in rs-fMRI outcome measures. If future studies, in a large number of participants, find significant annual periodicity in rsfMRI outcomes, then it would be prudent to take such temporal structure into consideration, especially when designing studies in chronic conditions, or for extended therapeutic interventions.”

Reason to rest easy?

The findings from Dr. Choe and colleagues’ ambitious study provides convincing evidence that the resting fMRI signal is reproducible over extensive time periods, giving reason for cognitive neuroscientists everywhere to breathe a small sigh of relief. Perhaps more importantly, it characterizes the nuanced patterns of its spatial and temporal stability, unraveling how it differs across brain networks and might be vulnerable to moderators such as aging or environment. This new understanding of fMRI dynamics will be incredibly useful to researchers aiming to optimize their fMRI study design, and holds particularly important implications for longitudinal studies in which aging or seasonal effects may be of concern. According to Dr. Choe,

“The high reproducibility of rs-fMRI network measures supports the candidacy of such measures as potential biomarkers for long-term therapeutic studies.”

One future application her team is currently pursuing is “using rs-fMRI to study brain reorganization in persons with chronic spinal cord injury, having recently reported significantly increased visuo-motor connectivity following recovery. We are interested in whether such measures can be used as biomarkers for prognosis and to help monitor responses to long-term therapy.”

References

Bandettini PA (2012). Twenty years of functional MRI: The science and the stories. Neuroimage. 62(2):575–588. doi: 10.1016/j.neuroimage.2012.04.026

Chen S, Ross TJ, Zhan W et al (2008). Group independent component analysis reveals consistent resting-state networks across multiple sessions. Brain Research. 1239:141-151. doi: 10.1016/j.brainres.2008.08.028

Choe AS, Belegu V, Yoshida S, Joet al (2013). Extensive neurological recovery from a complete spinal cord injury: a case report and hypothesis on the role of cortical plasticity. Front Hum Neurosci 7, 290.

Choe AS, Jones CK, Joel SE et al (2015). Reproducibility and Temporal Structure in Weekly Resting-State fMRI over a Period of 3.5 Years. PLOS One. 10(10):e0140134. doi: 10.1371/journal.pone.0140134

Guo CC, Kurth F, Zhou J et al (2012). One-year test–retest reliability of intrinsic connectivity network fMRI in older adults. Neuroimage. 61(4):1471–1483. doi: 10.1016/j.neuroimage.2012.03.027

Hodkinson DJ, O’Daly O, Zunzzain PA et al (2013). Circadian and homeostatic modulation of functional connectivity and regional cerebral blood flow in humans under normal entrained conditions. J Cereb Blood Flow & Metab. 34:1493–1499. doi: 10.1038/jcbfm.2014.109

Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A (2001). Neurophysiological investigation of the basis of the fMRI signal. Nature. 412:150–157. doi: 10.1038/nature35084005

Logothetis NK (2008). What we can do and what we cannot do with fMRI. Nature. 453:869–878. doi: 10.1038/nature06976

Wisner KM, Atluri G, Lim KO, MacDonald AW (2013). Neurometrics of intrinsic connectivity networks at rest using fMRI: Retest reliability and cross-validation using a meta-level method. Neuroimage. 76(1):236–251. doi: 10.1016/j.neuroimage.2013.02.066

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That All-Nighter is not without Neuroconsequences

Originally published on the PLOS Neuroscience Community

As you put the finishing touches on your paper, you notice the sun rising and fantasize about crawling in bed. Your vision and hearing are beginning to distort and the words staring back at you from the monitor have lost their meaning. Your brain … well, feels like mush. We’ve all been there. That debilitating brain fog that inevitably sets in after an all-nighter prompts the obvious question: what does sleep deprivation actually do to the brain? Neuroscientists from Norway set out to answer this question in their recent PLOS ONE study, examining how a night forgoing sleep affects brain microstructure. Among their findings, sleep deprivation induced widespread structural alterations throughout the brain. The lead author shares his thoughts on the possible biological causes of these changes, and whether they may be long-lasting.

Inducing sleep deprivation

The researchers assessed a group of 21 healthy young men over the course of a day. The participants underwent diffusion tensor imaging (DTI; a form of MRI that measures water diffusion and can be used to evaluate white matter integrity) when they first awoke, at 7:30 am. They were free to go about their day as normal before returning for a second DTI scan at 9:30 pm. They remained in the lab for monitoring until a final scan at 6:30 am the following morning, for a total period of 23 hours of continued waking. Since we’re now learning that anything and everything can influence brain structure on surprisingly short time-scales, the researchers finely controlled as many confounding factors as possible. The participants were not allowed to exercise or consume alcohol, caffeine or nicotine during the study, or to eat right before the scans. Since DTI measures water diffusion, hydration was evaluated at all sessions and accounted for in their analysis.

Rapid microstructural changes to waking

The researchers were interested in two main questions: How does the brain change after a normal day of wakefulness and after sleep deprivation? They focused on three DTI metrics to probe how different features of neuronal tissue may change with waking. Radial diffusivity (RD) measures how water diffuses across fibers, whereas axial diffusivity (AD) measures diffusion along the length of a tract. Fractional anisotropy (FA) is the ratio of axial to radial diffusivity and therefore measures how strongly water diffuses along a single direction.

From morning to evening, FA increased and this was driven mostly by reduced RD (Figure, left). From the evening to the next morning – after the all-nighter – FA values decreased to levels comparable to the prior morning, and this drop was coupled with a decrease in AD (Figure, right). Thus, over the course of a full day of wakefulness FA fluctuated, temporarily rising but eventually rebounding. In contrast, both RD and AD declined but at different rates, RD dropping by the end of a normal day, and AD dropping later, only after considerable sleep deprivation. These changes were non-specific, occurring throughout the brain, including in the corpus callosum, brainstem, thalamus and frontotemporal and parieto-occipital tracts.

Throughout the brain, FA values increase from morning to evening (left) and decrease from the evening to the next morning after a night without sleep (right). Elvsåshagen et al., 2015.

Throughout the brain, FA values increase from morning to evening (left) and decrease from the evening to the next morning after a night without sleep (right). Elvsåshagen et al., 2015.

How bad are the neuroconsequences of sleep deprivation?

Other studies have corroborated these reports that wakefulness alters the brain, including reduced diffusion with increasing time awake, and altered functional connectivity after sleep deprivation. How this plasticity reflects the consequences of waking on the brain, however, isn’t clear. Sleep is known to be essential to tissue repair and is particularly important for promoting lipid integrity to maintain healthy cell membranes and myelination. The question remains, therefore, how detrimental the structural reorganization from sleep deprivation really is. Does the plasticity reported here and elsewhere persist for days, weeks or longer, or can a long night of deep catch-up sleep reverse any detriment that all-nighter caused?

“My hypothesis,” says first author Dr. Torbjørn Elvsåshagen, “would be that the putative effects of one night of sleep deprivation on white matter microstructure are short term and reverse after one to a few nights of normal sleep. However, it could be hypothesized that chronic sleep insufficiency might lead to longer-lasting alterations in brain structure. Consistent with this idea, evidence for an association between impaired sleep and localized cortical thinning was found in obstructive sleep apnea syndrome, idiopathic rapid eye movement sleep behavior disorder, mild cognitive impairment and community-dwelling adults. Whether chronic sleep insufficiency can lead to longer-lasting alterations in white matter structure remains to be clarified.”

Is sleepiness really to blame?

It’s likely that multiple factors contribute to these distinct patterns of change in neuronal tissue. After sleep deprivation, the extent of AD decline correlated with subjective sleepiness ratings, suggesting that microstructural alterations may in fact be attributable to changes in alertness or arousal. This possibility is in line with the finding that changes occurred in both the thalamus and brainstem, regions important for arousal and wakefulness. However, the non-linear changes in FA suggest that some microstructural changes may be less related to sleepiness and more directly driven by circadian effects. FA increased late in the day, but – despite fatigue– dropped back after sleep deprivation to the same levels as the day prior. This rebounding may have been due to declining levels of AD and RD reaching equilibrium (reminder, FA is the ratio of AD to RD) or to neuronal features that fluctuate with our circadian rhythms, at least partially independent of our sleep habits. What’s more, other studies have found that presumably mundane activities, for example juggling or spatial learning, also induce gray and white matter changes within hours, and presumably many more as-of-yet unstudied activities also cause similarly rapid plasticity. Given that participants were free to engage in various physical and cognitive activities between the scans, it’s reasonable to assume that some of these behaviors may have also influenced brain structure. Whatever the mechanism, these effects underscore the importance of accounting for time of day in structural neuroimaging studies.

Dr. Elvsåshagen elaborates on these possible factors: “The precise neurobiological substrate for the observed DTI changes after waking remain to be clarified. We cannot rule out the possibility that both activity-independent and activity-dependent processes could contribute to DTI changes after waking. In support of potential activity-dependent white matter alterations, there is interesting evidence from in vitro studies indicating that hours of electrical activity can lead to changes in myelination. To further explore the possibility of activity-dependent white matter alterations, one could examine whether different physical or cognitive tasks lead to task-specific white matter changes.”

Sleepy outliers?

Notably, two of the 21 participants did not show the same rise in FA throughout the day as the others, and showed the smallest change in FA and AD after sleep deprivation. While variability across individuals in terms of brain structure and biological responses to the environment is expected, the remarkable consistency of the study’s other findings raises the possibility that some other factors may explain these outliers. Dr. Elvsåshagen conjectures, “These individuals were also the least tired individuals after sleep deprivation. Although highly speculative, one possible explanation for the lesser changes in these two participants might be a particular resistance to the effects of waking and sleep deprivation.” A follow-up with additional time-points and closer monitoring of activities could help more finely track how the patterns of brain microstructural change shift over periods of waking, and vary across individuals.

Linking diffusion to neurons

How sleep, fatigue, activity or circadian rhythms affect particular neuronal structural properties remains to be seen. RD and AD are thought to depend on myelin and axon integrity, respectively, but DTI metrics in general are sensitive to various other tissue features as well, including cell membrane permeability, axon diameter, tissue perfusion or glial processes. While these properties are difficult to image in living humans, insight from animal studies will help determine how waking impacts specific neuronal characteristics.

Longer-term studies are needed to answer these questions. Dr. Elvsåshagen shared that his team has since replicated their results in a larger sample, and are planning a follow-up study on the effects of waking and sleep deprivation on gray matter structure. Until these outstanding questions are answered, keeping a regular sleep schedule – and avoiding those early morning paper-writing marathons – may be better option for your brain health.

References

Bellesi M, Pfister-Genskow M, Maret S, Keles S, Tononi G, Cirelli C (2013). Effects of sleep and wake on oligodendrocytes and their precursors. J Neurosci. 33: 14288–14300. doi: 10.1523/JNEUROSCI.5102-12.2013

Budde MD, Xie M, Cross AH, Song SK (2009). Axial diffusivity is the primary correlate of axonal injury in the experimental autoimmune encephalomyelitis spinal cord: a quantitative pixelwise analysis. J Neurosci. 29: 2805–2813. doi: 10.1523/JNEUROSCI.4605-08.2009

De Havas JA, Parimal S, Soon CS, Chee MW (2012). Sleep deprivation reduces default mode network connectivity and anti-correlation during rest and task performance. NeuroImage. 59: 1745–1751. doi: 10.1016/j.neuroimage.2011.08.026

Driemeyer J, Boyke J, Gaser C, Buchel C, May A (2008). Changes in gray matter induced by learning—revisited. PLOS ONE. 3: e2669. doi: 10.1371/journal.pone.0002669

Elvsåshagen T, Norbom LB, Pedersen PØ, Quraishi SH, Bjørnerud A, Malt UK (2015). Widespread Changes in White Matter Microstructure after a Day of Waking and Sleep Deprivation. PLOS ONE. 10(5): e0124859. doi: 10.1371/journal.pone.0127351

Hinard V, et al. (2012). Key electrophysiological, molecular, and metabolic signatures of sleep and wakefulness revealed in primary cortical cultures. J Neurosci. 32: 12506–12517. doi: 10.1523/JNEUROSCI.2306-12.2012

Hofstetter S, Tavor I, Tzur Moryosef S, Assaf Y (2013). Short-term learning induces white matter plasticity in the fornix. J Neurosci. 33: 12844–12850. doi: 10.1523/JNEUROSCI.4520-12.2013

Jiang C, , et al. (2014). Diurnal microstructural variations in healthy adult brain revealed by diffusion tensor imaging. PLOS ONE. 9: e84822. doi: 10.1371/journal.pone.0084822

Joo EY, et al. (2013). Brain Gray Matter Deficits in Patients with Chronic Primary Insomnia. Sleep. 36(7): 999-1007. doi: 10.5665/sleep.2796

Rayayel S, et al. (2015). Patterns of cortical thinning in idiopathic rapid eye movement sleep behavior disorder. Mov Disord. 30(5): 680–687. doi: 10.1002/mds.25820

Sanchez-Espinosa MP, Atienza M, Cantero JL (2014). Sleep deficits in mild cognitive impairment are related to increased levels of plasma amyloid-β and cortical thinning. NeuroImage. 98: 395-404. doi: 10.1016/j.neuroimage.2014.05.027

Song SK, Sun SW, Ramsbottom MJ, Chang C, Russell J, Cross AH (2002). Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. NeuroImage. 17: 1429–1436. doi: 10.1006/nimg.2002.1267

Sexton CE, et al. (2014). Accelerated changes in white matter microstructure during aging: a longitudinal diffusion tensor imaging study. J Neurosci. 34(46): 15425–15436. doi: 10.1523/JNEUROSCI.0203-14.2014

Wake H, Lee PR, Fields RD (2011). Control of Local Protein Synthesis and Initial Events in Myelination by Action Potentials. Science. 333(6049): 1647–1651. doi: 10.1126/science.1206998

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How does Sports Training Restructure the Brain?

Originally published on the PLOS Neuroscience Community

The impact of regular exercise on the body is obvious. It improves cardiovascular fitness, increases strength and tones muscle. While these transformations are visible to the naked eye, changes to brain structure and function by physical activity occur behind the scenes and are therefore less understood. It’s not news that the brain is wonderfully plastic, dynamically reorganizing in response to every sensory, motor or cognitive experience. One might imagine therefore, that elite athletes–who train rigorously to perfect specialized movements–undergo robust neural adaptations that support, or reflect, their exceptional neuromuscular skills. Different sports, invoking different movements, will target unique neural substrates, but most physical activities similarly rely on regions that are key for eliciting, coordinating and controlling movement, such as the motor cortex, cerebellum and basal ganglia. In a new study published in Experimental Brain Research, Yu-Kai Chang and colleagues explored how microstructure in the basal ganglia reflects training and skill specialization of elite athletes.

Runners, martial artists and weekend warriors

The study enrolled groups of elite runners and elite martial artists, along with a control group of non-athletes who only engaged in occasional, casual exercise. Although both groups of athletes were highly trained (averaging over four hours of training daily), their uniquely specialized skills were key for determining whether basal ganglia structure varied by sport or by athletic training generally. The groups did not differ in terms of basic physical attributes, demographics or intelligence, but as expected, the athletes were more physically fit than the controls.

Measuring microstructure

The researchers focused on the basal ganglia, a set of nuclei comprising the caudate, putamen, globus pallidus, substantia nigra and subthalamic nuclei, since these structures serve critical roles in preparing for and executing movements and learning motor skills.

Structures of the basal ganglia

Structures of the basal ganglia

They used diffusion tensor imaging (DTI), which measures how water flows and diffuses within the brain. Since water diffusion is determined by neural features like axon density and myelination, it is more sensitive to finer-scale brain structure than traditional MRI approaches that measure the size or shape of brain regions. Fractional anisotropy (FA) and mean diffusivity (MD) are common metrics to assess, respectively, the directionality and amount of diffusion. Typically, higher FA and lower MD are thought to reflect higher integrity or greater organization of white matter.

Globus pallidus restructures in athletes

The basal ganglia microstructure of the athletes and controls were remarkably similar, with one exception. The internal globus pallidus showed lower FA and a trend for higher MD in the athletes than the non-athletes, but there were no differences between the runners and martial artists.

This result in intriguing for two reasons. First, it’s notable that both athletic groups showed a similar magnitude difference from non-athletes. Thus, acquiring and refining skilled movements more generally, rather than any particular movement pattern unique to running or martial arts, may restructure the globus pallidus. As study author Erik Chang explains,

“With the current results, we can only speculate that the experience of high intensity sport training, but not sport-specific factors, would trigger the localized changes in DTI indices we observed.”

This would make sense, considering the area is an important output pathway of the basal ganglia, broadly critical for learning and controlling movements. It’s likely that other regions may undergo more specialized adaptations to sport-specific training. Chang expects that future studies using a whole-brain approach with “distinctions between sport types and reasonable sample size would find cross-sectional differences or longitudinal changes in brain structure related to motor skill specialization.”

Second, although we expect athletic training to enhance regional brain structure, the reduced FA and increased MD observed in these elite athletes would commonly be considered signs of reduced white matter integrity. This is somewhat surprising in light of other studies reporting positive correlations between physical fitness and white matter integrity in non-professional athletes and children. But as Chang points out, “Professional sport experience is quite different from leisure training.” Although unexpected, this finding aligns well with similar reports that intensive training in dancersmusicians and multilinguals is associated with reduced gray or white matter volume or reduced FA. Why would this be? For starters, DTI doesn’t directly measure axonal integrity or myelination–only water diffusion. So while sports training has some clearly reorganizing effect on basal ganglia, we can’t yet infer what changes are occurring at the neuronal level. One interesting possibility is that the development of such expertise involves neuronal reorganization or pruning as circuits become more specialized and efficient. Chang cautions that their findings “could reflect the manifestation of an array of factors, including increased neural efficiency, altered cortical iron concentration in the elite athletes, or other training-specific/demographic variables.”

In the broader context, this study is a striking example of why care is warranted in interpreting neuroplasticity. Depending on the study conditions, the same intervention–here, athletic training–can apparently remodel the brain in opposing directions. This is an important reminder that although we like to assume that bigger is better in terms of brain structure, this is not always true, highlighting the need to more deeply explore exactly how and why these neural adaptations occur. Chang eagerly anticipates that future studies incorporating “HARDI (High-angular-resolution diffusion imaging) and Q-ball vector analysis, together with larger sample sizes and longitudinal design, will be very helpful in revealing finer microscopic structural differences among different types of elite athletes.”

References

Chang YK, Tsai JH, Wang CC and Chang EC (2015). Structural differences in basal ganglia of elite running versus martial arts athletes: a diffusion tensor imaging study. Exp Brain Res. doi: 10.1007/s00221-015-4293-x

Chaddock-Heyman L, et al. (2014). Aerobic fitness is associated with greater white matter integrity in children. Cortex. 54:179-89. doi: 10.1016/j.cortex.2014.02.014

Elmer S, Hänggi J and Jäncke L (2014). Processing demands upon cognitive, linguistic, and articulatory functions promote grey matter plasticity in the adult multilingual brain: Insights from simultaneous interpreters. Front Hum Neurosci. 8:584. doi: 10.3389/fnhum.2014.00584

Hänggi J, Koeneke S, Bezzola L and Jäncke L (2010). Structural neuroplasticity in the sensorimotor network of professional female ballet dancers. Hum Brain Mapp. 31(8):1196-206. doi:10.1002/hbm.20928

Imfeld A, et al. (2009). White matter plasticity in the corticospinal tract of musicians: a diffusion tensor imaging study. Neuroimage. 46(3):600-7. doi: 10.1016/j.neuroimage.2009.02.025

Tseng BY, et al. (2013). White matter integrity in physically fit older adults. Neuroimage. 82:510-6. doi: 10.1016/j.neuroimage.2013.06.011

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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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@PLOSNeuro #SfN14 Highlights: Intracranial EEG and Brain Stimulation

Originally published on the PLOS Neuroscience Community

Despite their many advantages, traditional tools to study neurocognitive function in humans, such as EEG or fMRI, carry several disadvantages compared to those usable on animals. Perhaps the most significant limitation is the challenge of imaging neural activity of live human brains during mental functions, which inherently requires the application of invasive neuroimaging techniques. Recently, the cognitive neuroscientist’s tool-belt has rapidly expanded, with the growing prevalence and usability of powerful imaging methods such as intracranial EEG – or electrocorticography (ECOG) – and electrical brain stimulation, that permit direct recording or stimulation of neuronal activity in live, conscious humans.

The SfN symposium Studying Human Cognition with Intracranial EEG and Electrical Brain Stimulation (previously previewed here, including an interview with speaker Josef Parvizi) explored current advances in these evolving methods along with their applications to the human cognitive experience.

Knight

UC Berkeley’s Bob Knight opened the symposium by highlighting the unique perks of ECOG over more traditional imaging techniques — points which were later recapitulated by other speakers — including its remarkably high spatial and temporal resolution and exceptional signal to noise ratio. ECOG is in fact so precise that it can reliably measure signal down to the single trial level – a feat neither EEG nor fMRI can boast. In just his brief introduction, Knight shared some impressive clinical and cognitive applications of these electrophysiology techniques. For instance, intracranial EEG signal from the auditory cortex was effective (with 99% accuracy!) at reconstructing words, holding clear implications for patients with speech impairments. My personal favorite highlight of the session, however, was the reconstruction of Pink Floyd’s “Another brick in the wall” from intracranial auditory cortex recordings.

Parvizi

First up, Josef Parvizi from Stanford University presented his lab’s multimodal approach to neurocognitive assessment, incorporating fMRI, ECOG and electrical brain stimulation. Parvizi shared a series of cases illustrating the powerful – and entertaining — applications of brain stimulation. In response to stimulation of the “salience network”, which had been previously mapped using fMRI, one patient responded that he felt like he was “riding in a storm”, but “felt nothing” after sham stimulation. A second patient reported the sense that “something bad is going to happen,” confirming in both patients emotionally driven reactions to “salience network” stimulation. In a final, particularly compelling, demonstration, Parvizi showed the effects of fusiform face area stimulation: “You just turned into somebody else,” the subject reported. “That was a trip!”

Malach

Next, Rafael Malach of the Weizmann Institute discussed his lab’s use of intracranial EEG to measure spontaneous neural activity at rest. FMRI is most commonly used to study resting-state activity; however, the BOLD signal may be contaminated by non-neural signal, and — due to its poor temporal resolution — is effectively blind to rapid events. Using ECOG, which overcomes both of these hurdles, Malach demonstrated how high frequency gamma activity accurately reflects neuronal firing rate and can assess functional connectivity. Surprisingly, spontaneous activity between recording sites on opposite hemispheres is more highly correlated than between adjacent recording sites. So ECOG may be a powerful tool for measuring spontaneous activity, but this is only valuable if we can identify the signal’s associated mental processes. Using the comical and celebrated example of the entorhinal cortex “Simpsons neuron”, which selectively fired in response to images of the Simpsons or immediately before spontaneous recall of the cartoon, Malach suggested that spontaneous activity exceeding an awareness threshold might indeed represent conscious thoughts.

Lachaux

Jean-Philippe Lachaux, from the Lyon Research Council, took a slightly different angle on the applications of ECOG, highlighting its unique suitability for evaluating naturalistic behavior. Because of its robustness against artifacts problematic in EEG or fMRI — like motion, blinking or signal distortion — ECOG can be more flexibly used in a variety of environments. These applications can be enhanced by integrating it with other tools such as eye-tracking, to more accurately associate natural behavior with neural activity in real time. Furthermore, Lachaux illustrated the power of ECOG at unraveling the temporal dynamics of functional interactions. Lachaux presented data questioning the common assumption that inter-region communication is typically a one-way street, proposing instead that such interactions may be more akin to reciprocal “shared conversations”.

Kastner

Sabine Kastner of Princeton University wrapped up the session with her lab’s comparative studies of attention in humans and monkeys. Combining human intracranial EEG with single-unit and LFP measures in monkeys during attention (Flanker task), she reported similar attention modulation in human and monkey intraparietal sulcus. Intriguingly, while attention modulated high gamma in both species, it also increased low frequency oscillations in humans. At the heart of cognitive neuroscience is the question of how neural activity translates to thoughts and behavior. To directly address this issue, Kastner is using electrophysiology to identify the optimal neural code for attention. In both humans and monkeys, she finds that spike phase better predicts behavior than spike rate, inching us one step closer to resolving the brain-cognition relationship.

Judging by the responses to my live-tweeting of this symposium, I’ll conjecture that the Neuro community is as intrigued and excited as yours-truly about the potential applications of ECOG and brain stimulation. In the words of @WiringTheBrain,

“This stuff is so COOL! And scary. But mainly COOL!”

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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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