Comparison of fMRI correlates of successful episodic memory encoding in temporal lobe epilepsy patients and healthy controls

fMRI evidence of movement familiarization effects on recognition memory in professional dancers

Dual-process theories propose that recognition memory involves recollection and familiarity; however, the impact of motor expertise on memory recognition, especially the interplay between familiarity and recollection, is relatively unexplored. This functional magnetic resonance imaging study used videos of a dancer performing International Latin Dance Styles as stimuli to investigate memory recognition in professional dancers and matched controls. Participants observed and then reported whether they recognized dance actions, recording the level of confidence in their recollections, whereas blood-oxygen-level-dependent signals measured encoding and recognition processes. Professional dancers showed higher accuracy and hit rates for high-confidence judgments, whereas matched controls exhibited the opposite trend for low-confidence judgments. The right putamen and precentral gyrus showed group-based moderation effects, especially for high-confidence (vs. low-confidence) action recognition in professional dancers. During action recognition, the right superior temporal gyrus and insula showed increased activation for accurate recognition and high-confidence retrieval, particularly in matched controls. These findings highlighting enhanced action memory of professional dancers-evident in their heightened recognition confidence-not only supports the dual-processing model but also underscores the crucial role of expertise-driven familiarity in bolstering successful recollection. Additionally, they emphasize the involvement of the action observation network and frontal brain regions in facilitating detailed encoding linked to intention processing.

Large-scale network dynamics underlying the first few hundred milliseconds after stimulus presentation: An investigation of visual recognition memory using iEEG

Recognition memory is the ability to recognize previously encountered objects. Even this relatively simple, yet extremely fast, ability requires the coordinated activity of large-scale brain networks. However, little is known about the sub-second dynamics of these networks. The majority of current studies into large-scale network dynamics is primarily based on imaging techniques suffering from either poor temporal or spatial resolution. We investigated the dynamics of large-scale functional brain networks underlying recognition memory at the millisecond scale. Specifically, we analyzed dynamic effective connectivity from intracranial electroencephalography while epileptic subjects (n = 18) performed a fast visual recognition memory task. Our data-driven investigation using Granger causality and the analysis of communities with the Louvain algorithm spotlighted a dynamic interplay of two large-scale networks associated with successful recognition. The first network involved the right visual ventral stream and bilateral frontal regions. It was characterized by early, predominantly bottom-up information flow peaking at 115 ms. It was followed by the involvement of another network with predominantly top-down connectivity peaking at 220 ms, mainly in the left anterior hemisphere. The transition between these two networks was associated with changes in network topology, evolving from a more segregated to a more integrated state. These results highlight that distinct large-scale brain networks involved in visual recognition memory unfold early and quickly, within the first 300 ms after stimulus onset. Our study extends the current understanding of the rapid network changes during rapid cognitive processes.

Dynamic causal modeling of reorganization of memory and language networks in temporal lobe epilepsy

OBJECTIVE

To evaluate the alterations of language and memory functions using dynamic causal modeling, in order to identify the epileptogenic hemisphere in temporal lobe epilepsy (TLE).

METHODS

Twenty-two patients with left TLE and 13 patients with right TLE underwent functional magnetic resonance imaging (fMRI) during four memory and four language mapping tasks. Dynamic causal modeling (DCM) was employed on fMRI data to examine effective directional connectivity in memory and language networks and the alterations in people with TLE compared to healthy individuals.

RESULTS

DCM analysis suggested that TLE can influence the memory network more widely compared to the language network. For memory mapping, it demonstrated overall hyperconnectivity from the left hemisphere to the other cranial regions in the picture encoding, and from the right hemisphere to the other cranial regions in the word encoding tasks. On the contrary, overall hypoconnectivity was seen from the brain hemisphere contralateral to the seizure onset in the retrieval tasks. DCM analysis further manifested hypoconnectivity between the brain's hemispheres in the language network in patients with TLE compared to controls. The CANTAB® neuropsychological test revealed a negative correlation for the left TLE and a positive correlation for the right TLE cohorts for the connections extracted by DCM that were significantly different between the left and right TLE cohorts.

INTERPRETATION

In this study, dynamic causal modeling evidenced the reorganization of language and memory networks in TLE that can be used for a better understanding of the effects of TLE on the brain's cognitive functions.

Machine learning classifiers for electrode selection in the design of closed-loop neuromodulation devices for episodic memory improvement

Successful neuromodulation approaches to alter episodic memory require closed-loop stimulation predicated on the effective classification of brain states. The practical implementation of such strategies requires prior decisions regarding electrode implantation locations. Using a data-driven approach, we employ support vector machine (SVM) classifiers to identify high-yield brain targets on a large data set of 75 human intracranial electroencephalogram subjects performing the free recall (FR) task. Further, we address whether the conserved brain regions provide effective classification in an alternate (associative) memory paradigm along with FR, as well as testing unsupervised classification methods that may be a useful adjunct to clinical device implementation. Finally, we use random forest models to classify functional brain states, differentiating encoding versus retrieval versus non-memory behavior such as rest and mathematical processing. We then test how regions that exhibit good classification for the likelihood of recall success in the SVM models overlap with regions that differentiate functional brain states in the random forest models. Finally, we lay out how these data may be used in the design of neuromodulation devices.

Direct brain recordings suggest a causal subsequent-memory effect

Endogenous variation in brain state and stimulus-specific evoked activity can both contribute to successful encoding. Previous studies, however, have not clearly distinguished among these components. We address this question by analysing intracranial EEG recorded from epilepsy patients as they studied and subsequently recalled lists of words. We first trained classifiers to predict recall of either single items or entire lists and found that both classifiers exhibited similar performance. We found that list-level classifier output-a biomarker of successful encoding-tracked item presentation and recall events, despite having no information about the trial structure. Across widespread brain regions, decreased low- and increased high-frequency activity (HFA) marked successful encoding of both items and lists. We found regional differences in the hippocampus and prefrontal cortex, where in the hippocampus HFA correlated more strongly with item recall, whereas, in the prefrontal cortex, HFA correlated more strongly with list performance. Despite subtle differences in item- and list-level features, the similarity in overall classification performance, spectral signatures of successful recall and fluctuations of spectral activity across the encoding period argue for a shared endogenous process that causally impacts the brain's ability to learn new information.

Riding brain "waves" to identify human memory genes

While there is extensive research on memory-related oscillations and brain gene expression, the relationship between oscillations and gene expression has rarely been studied. Recently, progress has been made to identify specific genes associated with oscillations that are correlated with episodic memory. Neocortical regions, in particular the temporal pole, have been examined in this line of research due to their accessibility during neurosurgical procedures. By harnessing this accessibility, a unique and powerful study design has allowed gene expression and intracranial oscillatory data to be sourced from the same human patients. These studies have identified a plethora of understudied gene targets that should be further characterized with respect to human brain function. Future work should extend to other brain regions to increase our understanding of the genetic signatures of oscillations and, ultimately, human cognition.

Hotspot of human verbal memory encoding in the left anterior prefrontal cortex

Background

Treating memory and cognitive deficits requires knowledge about anatomical sites and neural activities to be targeted with particular therapies. Emerging technologies for local brain stimulation offer attractive therapeutic options but need to be applied to target specific neural activities, at distinct times, and in specific brain regions that are critical for memory formation.

Methods

The areas that are critical for successful encoding of verbal memory as well as the underlying neural activities were determined directly in the human brain with intracranial electrophysiological recordings in epilepsy patients. We recorded a broad range of spectral activities across the cortex of 135 patients as they memorised word lists for subsequent free recall.

Findings

The greatest differences in the spectral power between encoding subsequently recalled and forgotten words were found in low theta frequency (3–5 Hz) activities of the left anterior prefrontal cortex. This subsequent memory effect was proportionally greater in the lower frequency bands and in the more anterior cortical regions. We found the peak of this memory signal in a distinct part of the prefrontal cortex at the junction between the Broca's area and the frontal pole. The memory effect in this confined area was significantly higher (Tukey–Kramer test, p<0.05) than in other anatomically distinct areas.

Interpretation

Our results suggest a focal hotspot of human verbal memory encoding located in the higher-order processing region of the prefrontal cortex, which presents a prospective target for modulating cognitive functions in the human patients. The memory effect provides an electrophysiological biomarker of low frequency neural activities, at distinct times of memory encoding, and in one hotspot location in the human brain.

Funding

Open-access datasets were originally collected as part of a BRAIN Initiative project called Restoring Active Memory (RAM) funded by the Defence Advanced Research Project Agency (DARPA). CT, ML, MTK and this research were supported from the First Team grant of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund.

Changes in white matter functional networks during wakefulness and sleep

Blood oxygenation level‐dependent (BOLD) signals in the white matter (WM) have been demonstrated to encode neural activities by showing structure‐specific temporal correlations during resting‐state and task‐specific imaging of fiber pathways with various degrees of correlations in strength and time delay. Previous neuroimaging studies have shown state‐dependent functional connectivity and regional amplitude of signal fluctuations in brain gray matter across wakefulness and nonrapid eye movement (NREM) sleep cycles. However, the functional characteristics of WM during sleep remain unknown. Using simultaneous electroencephalography and functional magnetic resonance imaging data during wakefulness and NREM sleep collected from 66 healthy participants, we constructed 10 stable WM functional networks using clustering analysis. Functional connectivity between these WM functional networks and regional amplitude of WM signal fluctuations across multiple low‐frequency bands were evaluated. In general, decreased WM functional connectivity between superficial and middle layer WM functional networks was observed from wakefulness to sleep. In addition, functional connectivity between the deep and cerebellar networks was higher during light sleep and lower during both wakefulness and deep sleep. The regional fluctuation amplitude was always higher during light sleep and lower during deep sleep. Importantly, slow‐wave activity during deep sleep negatively correlated with functional connectivity between WM functional networks but positively correlated with fluctuation strength in the WM. These observations provide direct physiological evidence that neural activities in the WM are modulated by the sleep–wake cycle. This study provided the initial mapping of functional changes in WM during sleep.

Dynamic functional connectivity in modular organization of the hippocampal network marks memory phenotypes in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is a network disorder with a high incidence of memory impairment. Memory processing ability highly depends on the dynamic coordination between distinct modules within the hippocampal network. Here, we investigate the relationship between memory phenotypes and modular alterations of dynamic functional connectivity (FC) in the hippocampal network in TLE patients. Then, 31 healthy controls and 66 TLE patients with hippocampal sclerosis were recruited. The patients were classified into memory-intact (MI, 35 cases) group and memory-deficit (MD, 31 cases) group, each based on individual's Wechsler Memory Scale-Revised score. The sliding-windows approach and graph theory analysis were used to analyze the hippocampal network based on resting state functional magnetic resonance imaging. Temporal properties and modular metrics were calculated. Two discrete and switchable states were revealed: a high modularized state (State I) and a low modularized state (State II), which corresponded to either anterior or posterior hippocampal network dominated pattern. TLE was prone to drive less State I but more State II, and the tendency was more obvious in TLE-MD. Additionally, TLE-MD showed more widespread alterations of modular properties compared with TLE-MI across two states. Furthermore, the dynamic modularity features had unique superiority in discriminating TLE-MD from TLE-MI. These findings demonstrated that state transitions and modular function of dissociable hippocampal networks were altered in TLE and more importantly, they could reflect different memory phenotypes. The trend revealed potential values of dynamic FC in elucidating the mechanism underlying memory impairments in TLE.

Distinct Neurophysiological Correlates of the fMRI BOLD Signal in the Hippocampus and Neocortex

Functional magnetic resonance imaging (fMRI) is among the foremost methods for mapping human brain function but provides only an indirect measure of underlying neural activity. Recent findings suggest that the neurophysiological correlates of the fMRI blood oxygenation level-dependent (BOLD) signal might be regionally specific. We examined the neurophysiological correlates of the fMRI BOLD signal in the hippocampus and neocortex, where differences in neural architecture might result in a different relationship between the respective signals. Fifteen human neurosurgical patients (10 female, 5 male) implanted with depth electrodes performed a verbal free recall task while electrophysiological activity was recorded simultaneously from hippocampal and neocortical sites. The same patients subsequently performed a similar version of the task during a later fMRI session. Subsequent memory effects (SMEs) were computed for both imaging modalities as patterns of encoding-related brain activity predictive of later free recall. Linear mixed-effects modeling revealed that the relationship between BOLD and gamma-band SMEs was moderated by the lobar location of the recording site. BOLD and high gamma (70-150 Hz) SMEs positively covaried across much of the neocortex. This relationship was reversed in the hippocampus, where a negative correlation between BOLD and high gamma SMEs was evident. We also observed a negative relationship between BOLD and low gamma (30-70 Hz) SMEs in the medial temporal lobe more broadly. These results suggest that the neurophysiological correlates of the BOLD signal in the hippocampus differ from those observed in the neocortex.SIGNIFICANCE STATEMENT The BOLD signal forms the basis of fMRI but provides only an indirect measure of neural activity. Task-related modulation of BOLD signals are typically equated with changes in gamma-band activity; however, relevant empirical evidence comes largely from the neocortex. We examined neurophysiological correlates of the BOLD signal in the hippocampus, where the differing neural architecture might result in a different relationship between the respective signals. We identified a positive relationship between encoding-related changes in BOLD and gamma-band activity in the frontal and parietal cortices. This effect was reversed in the hippocampus, where BOLD and gamma-band effects negatively covaried. These results suggest regional variability in the transfer function between neural activity and the BOLD signal in the hippocampus and neocortex.

Whole-brain functional correlates of memory formation in mesial temporal lobe epilepsy

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Large study of encoding and subsequent memory for words, faces, and scenes.

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Less ipsilateral mesial temporal activity in mesial temporal lobe epilepsy (mTLE).

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Extra-mTL activity in mTLE only partly relevant for memory formation.

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Across materials contralateral mTL decisive to maintain intact memory in mTLE.

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Left frontal activation correlates with better verbal memory only in left mTLE.

The mesial temporal lobe is a key region for episodic memory. Accordingly, memory impairment is frequent in patients with mesial temporal lobe epilepsy. However, the functional relevance of potentially epilepsy-induced reorganisation for memory formation is still not entirely clear. Therefore, we investigated whole-brain functional correlates of verbal and non-verbal memory encoding and subsequent memory formation in 56 (25 right sided) mesial temporal lobe epilepsy patients and 21 controls. We applied an fMRI task of learning scenes, faces, and words followed by an out-of-scanner recognition test. During encoding of faces and scenes left and right mesial temporal lobe epilepsy patients had consistently reduced activation in the epileptogenic mesial temporal lobe compared with controls. Activation increases in patients were apparent in extra-temporal regions, partly associated with subsequent memory formation (left frontal regions and basal ganglia), and patients had less deactivation in regions often linked to the default mode and auditory networks. The more specific subsequent memory contrast indicated only marginal group differences. Correlating patients’ encoding activation with memory performance both within the paradigm and with independent clinical measures demonstrated predominantly increased contralateral mesio-temporal activation supporting intact memory performance. In left temporal lobe epilepsy patients, left frontal activation was also correlated with better verbal memory performance. Taken together, our findings hint towards minor extra-temporal plasticity in mesial temporal lobe epilepsy patients, which is in line with pre-surgical impairment and post-surgical memory decline in many patients. Further, data underscore the importance of particularly the contralateral mesial temporal lobe itself, to maintain intact memory performance.

Recent Advances in Neuroimaging of Epilepsy

Human neuroimaging has had a major impact on the biological understanding of epilepsy and the relationship between pathophysiology, seizure management, and outcomes. This review highlights notable recent advancements in hardware, sequences, methods, analyses, and applications of human neuroimaging techniques utilized to assess epilepsy. These structural, functional, and metabolic assessments include magnetic resonance imaging (MRI), positron emission tomography (PET), and magnetoencephalography (MEG). Advancements that highlight non-invasive neuroimaging techniques used to study the whole brain are emphasized due to the advantages these provide in clinical and research applications. Thus, topics range across presurgical evaluations, understanding of epilepsy as a network disorder, and the interactions between epilepsy and comorbidities. New techniques and approaches are discussed which are expected to emerge into the mainstream within the next decade and impact our understanding of epilepsies. Further, an increasing breadth of investigations includes the interplay between epilepsy, mental health comorbidities, and aberrant brain networks. In the final section of this review, we focus on neuroimaging studies that assess bidirectional relationships between mental health comorbidities and epilepsy as a model for better understanding of the commonalities between both conditions.

Gene-expression correlates of the oscillatory signatures supporting human episodic memory encoding

In humans, brain oscillations support critical features of memory formation. However, understanding the molecular mechanisms underlying this activity remains a major challenge. Here, we measured memory-sensitive oscillations using intracranial electroencephalography recordings from the temporal cortex of patients performing an episodic memory task. When these patients subsequently underwent resection, we employed transcriptomics on the temporal cortex to link gene expression with brain oscillations and identified genes correlated with oscillatory signatures of memory formation across six frequency bands. A co-expression analysis isolated oscillatory signature-specific modules associated with neuropsychiatric disorders and ion channel activity, with highly correlated genes exhibiting strong connectivity within these modules. Using single-nucleus transcriptomics, we further revealed that these modules are enriched for specific classes of both excitatory and inhibitory neurons, and immunohistochemistry confirmed expression of highly correlated genes. This unprecedented dataset of patient-specific brain oscillations coupled to genomics unlocks new insights into the genetic mechanisms that support memory encoding.

Effects of Age on Prestimulus Neural Activity Predictive of Successful Memory Encoding: An fMRI Study

Prestimulus subsequent memory effects (SMEs)-differences in neural activity preceding the onset of study items that are predictive of later memory performance-have consistently been reported in young adults. The present functional magnetic resonance imaging experiment investigated potential age-related differences in prestimulus SMEs. During study, healthy young and older participants made one of two semantic judgments on images, with the judgment signaled by a preceding cue. In test phase, participants first made an item recognition judgment and, for each item judged old, a source memory judgment. Age-invariant prestimulus SMEs were observed in left dorsomedial prefrontal cortex, left hippocampus, and right subgenual cortex. In each case, the effects reflected lower blood oxygen level dependent signal for later recognized items, regardless of source accuracy, than for unrecognized items. A similar age-invariant pattern was observed in left orbitofrontal cortex, but this effect was specific to items attracting a correct source response compared to unrecognized items. In contrast, the left angular gyrus and fusiform cortex demonstrated negative prestimulus SMEs that were exclusive to young participants. The findings indicate that age differences in prestimulus SMEs are regionally specific and suggest that prestimulus SMEs reflect multiple cognitive processes, only some of which are vulnerable to advancing age.

Contribution of left supramarginal and angular gyri to episodic memory encoding: An intracranial EEG study

The role of the left ventral lateral parietal cortex (VPC) in episodic memory is hypothesized to include bottom-up attentional orienting to recalled items, according to the dual-attention model (Cabeza et al., 2008). However, its role in memory encoding could be further clarified, with studies showing both positive and negative subsequent memory effects (SMEs). Furthermore, few studies have compared the relative contributions of sub-regions in this functionally heterogeneous area, specifically the anterior VPC (supramarginal gyrus/BA40) and the posterior VPC (angular gyrus/BA39), on a within-subject basis. To elucidate the role of the VPC in episodic encoding, we compared SMEs in the intracranial EEG across multiple frequency bands in the supramarginal gyrus (SmG) and angular gyrus (AnG), as twenty-four epilepsy patients with indwelling electrodes performed a free recall task. We found a significant SME of decreased theta power and increased high gamma power in the VPC overall, and specifically in the SmG. Furthermore, SmG exhibited significantly greater spectral tilt SME from 0.5 to 1.6 s post-stimulus, in which power spectra slope differences between recalled and unrecalled words were greater than in the AnG (p = 0.04). These results affirm the contribution of VPC to episodic memory encoding, and suggest an anterior-posterior dissociation within VPC with respect to its electrophysiological underpinnings.

Direct brain recordings identify hippocampal and cortical networks that distinguish successful versus failed episodic memory retrieval

Human data collected using noninvasive imaging techniques have established the importance of parietal regions towards episodic memory retrieval, including the angular gyrus and posterior cingulate cortex. Such regions comprise part of a putative core episodic retrieval network. In free recall, comparisons between contextually appropriate and inappropriate recall events (i.e. prior list intrusions) provide the opportunity to study memory retrieval networks supporting veridical recall, and existing findings predict that differences in electrical activity in these brain regions should be identified according to the accuracy of recall. However, prior iEEG studies, utilizing principally subdural grid electrodes, have not fully characterized brain activity in parietal regions during memory retrieval and have not examined connectivity between core recollection areas and the hippocampus or prefrontal cortex. Here, we employed a data set obtained from 100 human patients implanted with stereo EEG electrodes for seizure mapping purposes as they performed a free recall task. This data set allowed us to separately analyze activity in midline versus lateral parietal brain regions, and in anterior versus posterior hippocampus, to identify areas in which retrieval-related activity predicted the recollection of a correct versus an incorrect memory. With the wide coverage afforded by the stereo EEG approach, we were also able to examine interregional connectivity. Our key findings were that differences in gamma band activity in the angular gyrus, precuneus, posterior temporal cortex, and posterior (more than anterior) hippocampus discriminated accurate versus inaccurate recall as well as active retrieval versus memory search. The left angular gyrus exhibited a significant power decrease preceding list intrusions as well as unique phase-amplitude coupling properties, whereas the prefrontal cortex was unique in exhibiting a power increase during list intrusions. Analysis of connectivity revealed significant hemispheric asymmetry, with relatively sparse left-sided functional connections compared to the right hemisphere. One exception to this finding was elevated connectivity between the prefrontal cortex and left angular gyrus. This finding is interpreted as evidence for the engagement of prefrontal cortex in memory monitoring and mnemonic decision-making.