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Post-COVID syndrome manifests with numerous neurological and cognitive symptoms, the precise origins of which are still not fully understood. As females and older adults are more susceptible to developing this condition, our study aimed to investigate how post-COVID syndrome alters intrinsic brain dynamics in older adults and whether biological sex and cognitive training might modulate these effects, with a specific focus on older females. The participants, aged between 60 and 75 years, were divided into three experimental groups: healthy old female, post-COVID old female and post-COVID old male. They underwent an adaptive task-switching training protocol. We analysed multiscale entropy and spectral power density of resting-state EEG data collected before and after the training to assess neural signal complexity and oscillatory power, respectively. We found no difference between post-COVID females and males before training, indicating that post-COVID similarly affected both sexes. However, cognitive training was effective only in post-COVID females and not in males, by modulating local neural processing capacity. This improvement was further evidenced by comparing healthy and post-COVID females, wherein the latter group showed increased finer timescale entropy (1-30 ms) and higher frequency band power (11-40 Hz) before training, but these differences disappeared following cognitive training. Our results suggest that in older adults with post-COVID syndrome, there is a pronounced shift from more global to local neural processing, potentially contributing to accelerated neural aging in this condition. However, cognitive training seems to offer a promising intervention method for modulating these changes in brain dynamics, especially among females., (© 2024. The Author(s).)

Clinical studies of major depression (MD) generally focus on group effects, yet interindividual differences in brain function are increasingly recognized as important and may even impact effect sizes related to group effects. Here, we examine the magnitude of individual differences in relation to group differences that are commonly investigated (e.g., related to MD diagnosis and treatment response). Functional MRI data from 107 participants (63 female, 44 male) were collected at baseline, 2, and 8 weeks during which patients received pharmacotherapy (escitalopram, N  = 68) and controls ( N  = 39) received no intervention. The unique contributions of different sources of variation were examined by calculating how much variance in functional connectivity was shared across all participants and sessions, within/across groups (patients vs controls, responders vs nonresponders, female vs male participants), recording sessions, and individuals. Individual differences and common connectivity across groups, sessions, and participants contributed most to the explained variance (>95% across analyses). Group differences related to MD diagnosis, treatment response, and biological sex made significant but small contributions (0.3-1.2%). High individual variation was present in cognitive control and attention areas, while low individual variation characterized primary sensorimotor regions. Group differences were much smaller than individual differences in the context of MD and its treatment. These results could be linked to the variable findings and difficulty translating research on MD to clinical practice. Future research should examine brain features with low and high individual variation in relation to psychiatric symptoms and treatment trajectories to explore the clinical relevance of the individual differences identified here., Competing Interests: B.N.F. has received grant/research support from Alternative Funding Plan Innovations Award, Brain and Behavior Research Foundation, Canadian Institutes of Health Research, Hamilton Health Sciences Foundation, J. P. Bickell Foundation, Ontario Brain Institute, Ontario Mental Health Foundation, Society for Women's Health Research, Teresa Cascioli Charitable Foundation, Eli Lilly, and Pfizer and has received consultant and/or speaker fees from AstraZeneca, Bristol-Myers Squibb, Canadian Psychiatric Association, CANMAT, Daiichi Sankyo, Lundbeck, Pfizer, Servier, and Sunovion. R.M. has received consulting and speaking honoraria from AbbVie, Allergan, Janssen, KYE, Lundbeck, Otsuka, and Sunovion and research grants from CAN-BIND, CIHR, Janssen, Lallemand, Lundbeck, Nubiyota, OBI, and OMHF. S.P. has been a consultant to Takeda, Bristol Myers Squibb, Lundbeck; has had a research contract with Assurex; and has equity in Mensante. R.W.L. has received speaker and consultant honoraria or research funds from AstraZeneca, Brain Canada, Bristol-Myers Squibb, the Canadian Institutes of Health Research (CIHR), the Canadian Network for Mood and Anxiety Treatments, the Canadian Psychiatric Association, Eli Lilly, Janssen, Lundbeck, Lundbeck Institute, Medscape, Otsuka, Pfizer, Servier, St. Jude Medical, Takeda, the University Health Network Foundation, Vancouver Coastal Health Research Institute, Allergan, Asia-Pacific Economic Cooperation, BC Leading Edge Foundation, Healthy Minds Canada, Michael Smith Foundation for Health Research, MITACS, Myriad Neuroscience, Ontario Brain Institute, Otsuka, Unity Health, Viatris, and VGH-UBCH Foundation. S.H.K. has received honoraria or research funds from Abbott, Alkermes, Allergan, Boehringer Ingelheim, Brain Canada, CIHR, Janssen, Lundbeck, Lundbeck Institute, Ontario Brain Institute, Ontario Research Fund, Otsuka, Pfizer, Servier, Sunovion, and Sun Pharmaceutical and holds stock in Field Trip Health. D.J.M. has received consulting and speaking honoraria from Lundbeck and Genomind. C.S. has received consulting and speaking honoraria from Pfizer, Otsuka, Bayer, Eisai, and research grants from CAN-BIND, CIHR, OBI, and SEAMO. S.C.S. is a senior Scientific Advisor and shareholder in ADMdx, which receives NIH funding, and during the period of this research, he had research grants from Brain Canada, Canada Foundation for Innovation (CFI), Canadian Institutes of Health Research (CIHR), and the Ontario Brain Institute in Canada. Other authors declare no competing financial interests., (Copyright © 2024 van der Wijk et al.)

Extracting repeated patterns from our surroundings plays a crucial role in contextualizing information, making predictions, and guiding our behavior implicitly. Previous research showed that contextual cueing enhances visual search performance in younger adults. In this study, we investigated whether contextual cueing could also improve older adults' performance and whether age-related differences in the neural processes underlying implicit contextual learning could be detected. Twenty-four younger and 25 older participants performed a visual search task with contextual cueing. Contextual information was generated using repeated face configurations alongside random new configurations. We measured RT difference between new and repeated configurations; ERPs to uncover the neural processes underlying contextual cueing for early (N2pc), intermediate (P3b), and late (r-LRP) processes; and multiscale entropy and spectral power density analyses to examine neural dynamics. Both younger and older adults showed similar contextual cueing benefits in their visual search efficiency at the behavioral level. In addition, they showed similar patterns regarding contextual information processing: Repeated face configurations evoked decreased finer timescale entropy (1-20 msec) and higher frequency band power (13-30 Hz) compared with new configurations. However, we detected age-related differences in ERPs: Younger, but not older adults, had larger N2pc and P3b components for repeated compared with new configurations. These results suggest that contextual cueing remains intact with aging. Although attention- and target-evaluation-related ERPs differed between the age groups, the neural dynamics of contextual learning were preserved with aging, as both age groups increasingly utilized more globally grouped representations for repeated face configurations during the learning process., (© 2024 Massachusetts Institute of Technology.)

Introduction: To date, no robust electroencephalography (EEG) markers of antidepressant treatment response have been identified. Variable findings may arise from the use of group analyses, which neglect individual variation. Using a combination of group and single-participant analyses, we explored individual variability in EEG characteristics of treatment response., Methods: Resting-state EEG data and Montgomery-Åsberg Depression Rating Scale (MADRS) symptom scores were collected from 43 patients with depression before, at 1 and 12 weeks of pharmacotherapy. Partial least squares (PLS) was used to: 1) identify group differences in EEG connectivity (weighted phase lag index) and complexity (multiscale entropy) between eventual medication responders and non-responders, and 2) determine whether group patterns could be identified in individual patients., Results: Responders showed decreased alpha and increased beta connectivity, and early, widespread decreases in complexity over treatment. Non-responders showed an opposite connectivity pattern, and later, spatially confined decreases in complexity. Thus, as in previous studies, our group analyses identified significant differences between groups of patients with different treatment outcomes. These group-level EEG characteristics were only identified in ~40-60% of individual patients, as assessed quantitatively by correlating the spatiotemporal brain patterns between groups and individual results, and by independent raters through visualization., Discussion: Our single-participant analyses suggest that substantial individual variation exists, and needs to be considered when investigating characteristics of antidepressant treatment response for potential clinical applicability., Clinical Trial Registration: https://clinicaltrials.gov, identifier NCT00519428., Competing Interests: PB received honoraria for lectures and/or participation in advisory boards for Allergan, Janssen, Lundbeck, Otsuka, Pfizer, Pierre Fabre Médicaments, Sunovion and Takeda. He has provided expert testimony on behalf of Bristol Myers Squibb and Otsuka. These industries had no influence on the work presented herein. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 van der Wijk, Enkhbold, Cnudde, Szostakiwskyj, Blier, Knott, Jaworska and Protzner.)

By combining transcranial magnetic stimulation (TMS) with electroencephalography, human cortical circuits can be directly interrogated. The resulting electrical trace contains TMS-evoked potential (TEP) components, and it is not known whether the amplitudes of these components are stimulus intensity dependent. We examined this in the left dorsolateral prefrontal cortex in nineteen healthy adult participants and extracted TEP amplitudes for the N40, P60, N120, and P200 components at 110%, 120%, and 130% of resting motor threshold (RMT). To probe plasticity of putative stimulus intensity dose-response relationships, this was repeated after participants received intermittent theta burst stimulation (iTBS; 600 pulses, 80% RMT). The amplitude of the N120 and P200 components exhibited a stimulus intensity dose-response relationship, however the N40 and P60 components did not. After iTBS, the N40 and P60 components continued to exhibit a lack of stimulus intensity dose-dependency, and the P200 dose-response was unchanged. In the N120 component, however, we saw evidence of change within the stimulus intensity dose-dependent relationship characterized by a decrease in absolute peak amplitudes at lower stimulus intensities. These data suggest that TEP components have heterogeneous dose-response relationships, with implications for standardizing and harmonizing methods across experiments. Moreover, the selective modification of the N120 dose-response relationship may provide a novel marker for iTBS plasticity in health and disease., (© 2023. The Author(s).)

Deep brain stimulation (DBS) has shown therapeutic benefits for treatment resistant depression (TRD). Stimulation of the subcallosal cingulate gyrus (SCG) aims to alter dysregulation between subcortical and cortex. However, the 50% response rates for SCG-DBS indicates that selection of appropriate patients is challenging. Since stimulation influences large-scale network function, we hypothesized that network features can be used as biomarkers to inform outcome. In this pilot project, we used resting-state EEG recorded longitudinally from 10 TRD patients with SCG-DBS (11 at baseline). EEGs were recorded before DBS-surgery, 1-3 months, and 6 months post surgery. We used graph theoretical analysis to calculate clustering coefficient, global efficiency, eigenvector centrality, energy, and entropy of source-localized EEG networks to determine their topological/dynamical features. Patients were classified as responders based on achieving a 50% or greater reduction in Hamilton Depression (HAM-D) scores from baseline to 12 months post surgery. In the delta band, false discovery rate analysis revealed that global brain network features (segregation, integration, synchronization, and complexity) were significantly lower and centrality of subgenual anterior cingulate cortex (ACC) was higher in responders than in non-responders. Accordingly, longitudinal analysis showed SCG-DBS increased global network features and decreased centrality of subgenual ACC. Similarly, a clustering method separated two groups by network features and significant correlations were identified longitudinally between network changes and depression symptoms. Despite recent speculation that certain subtypes of TRD are more likely to respond to DBS, in the SCG it seems that underlying brain network features are associated with ability to respond to DBS. SCG-DBS increased segregation, integration, and synchronizability of brain networks, suggesting that information processing became faster and more efficient, in those patients in whom it was lower at baseline. Centrality results suggest these changes may occur via altered connectivity in specific brain regions especially ACC. We highlight potential mechanisms of therapeutic effect for SCG-DBS., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Our attention often drifts away from the ongoing task to task-unrelated thoughts, a phenomenon commonly referred to as mind wandering. Ample studies dedicated to delineating its electrophysiological correlates have revealed distinct event-related potentials (ERP) and spectral patterns associated with mind wandering. It remains less clear whether the complexity of the electroencephalography (EEG) changes when our minds wander, a metric that captures the predictability of the time series at varying timescales. Accordingly, this study investigated whether mind wandering impacts EEG signal complexity. We further explored whether such effects differ across timescales, and change in a context-dependent manner as indexed by global and local levels of processing. To address this, we recorded participants' EEG while they completed Navon's global and local processing task and occasionally reported whether they were on-task or mind wandering throughout the task. We found that brain signal complexity as indexed by multiscale entropy decreased at medium timescales in centro-parietal regions and increased at coarse timescales in anterior and posterior regions during mind wandering, as compared to the on-task state, for global processing. Moreover, global processing showed increased complexity at fine to medium timescales compared to local processing. Finally, behavioral performance revealed a context-dependent effect in accuracy measures, with mind wandering showing lower accuracy compared to the on-task state only during the local condition. Taken together, these results indicate that changes in brain signal complexity across timescales may be an important feature of mind wandering., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Many theories of semantic representation propose that simulations of sensorimotor experience contribute to language processing. This can be seen in the body-object interaction effect (BOI; how easily the human body can interact with a word's referent). Words with high BOI ratings (e.g., ball ) are processed more quickly than words with low BOI ratings (e.g., cloud ) in various language tasks. This effect can be modulated by task demands. Previous research established that when asked to decide if a word is an object (entity condition), a BOI effect is observed, but when asked to decide if a word is an action (action condition), there is no BOI effect. It is unclear whether the null behavioral effect in the action condition reflects top-down modulation of task-relevant sensorimotor information or the absence of bottom-up activation of sensorimotor simulations. We investigated this question using EEG. In Experiment 1 we replicated the previous behavioral findings. In Experiment 2, 50 participants were assigned to either the entity or action conditions and responded to the same word stimuli. In both conditions we observed differences in ERP components related to the BOI effect. In the entity condition the P2 mean amplitude was significantly more positive for high compared to low BOI words. In the action condition the N400 peak latency was significantly later for high compared to low BOI words. Our findings suggest that BOI information is generated bottom-up regardless of task demands and modulated by top-down processes that recruit sensorimotor information relevant to the task decision., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Muraki, Doyle, Protzner and Pexman.)

Recent work suggests that the relationship between age and memory-related brain activity are different for men and women. We sought to extend this work by examining sex differences in the association between age, memory performance, and brain signal variability during context memory tasks in neurotypical adults (aged 19-76 years; N = 128, 87 women). We measured blood oxygen level-dependent standard deviation (BOLD SD) during encoding and retrieval in easy and difficult spatial context memory tasks and investigated sex-specific, age- and performance-associated BOLD SD patterns. Behavioral analysis revealed age-related decreases in memory retrieval, but no sex differences nor an age-by-sex interaction. Imaging results indicated that both sexes showed a negative correlation between BOLD SD and retrieval accuracy in memory-related regions. We also identified significant sex differences: women exhibited age-associated increases in BOLD SD which were negatively associated with performance. Men exhibited both age-associated decreases and increases, which were not related to performance. Our results revealed sex differences in the relationship between age and BOLD SD during high-demand episodic memory tasks., (Copyright © 2022 Elsevier Inc. All rights reserved.)

With increasing life expectancy and active aging, it becomes crucial to investigate methods which could compensate for generally detected cognitive aging processes. A promising candidate is adaptive cognitive training, during which task difficulty is adjusted to the participants' performance level to enhance the training and potential transfer effects. Measuring intrinsic brain activity is suitable for detecting possible distributed training-effects since resting-state dynamics are linked to the brain's functional flexibility and the effectiveness of different cognitive processes. Therefore, we investigated if adaptive task-switching training could modulate resting-state neural dynamics in younger (18-25 years) and older (60-75 years) adults (79 people altogether). We examined spectral power density on resting-state EEG data for measuring oscillatory activity, and multiscale entropy for detecting intrinsic neural complexity. Decreased coarse timescale entropy and lower frequency band power as well as increased fine timescale entropy and higher frequency band power revealed a shift from more global to local information processing with aging before training. However, cognitive training modulated these age-group differences, as coarse timescale entropy and lower frequency band power increased from pre- to post-training in the old-training group. Overall, our results suggest that cognitive training can modulate neural dynamics even when measured outside of the trained task., (© 2022. The Author(s).)

Over the past 15 years, deep brain stimulation (DBS) has been actively investigated as a groundbreaking therapy for patients with treatment-resistant depression (TRD); nevertheless, outcomes have varied from patient to patient, with an average response rate of ∼50%. The engagement of specific fiber tracts at the stimulation site has been hypothesized to be an important factor in determining outcomes, however, the resulting individual network effects at the whole-brain scale remain largely unknown. Here we provide a computational framework that can explore each individual's brain response characteristics elicited by selective stimulation of fiber tracts. We use a novel personalized in-silico approach, the Virtual Big Brain, which makes use of high-resolution virtual brain models at a mm-scale and explicitly reconstructs more than 100,000 fiber tracts for each individual. Each fiber tract is active and can be selectively stimulated. Simulation results demonstrate distinct stimulus-induced event-related potentials as a function of stimulation location, parametrized by the contact positions of the electrodes implanted in each patient, even though validation against empirical patient data reveals some limitations (i.e., the need for individual parameter adjustment, and differential accuracy across stimulation locations). This study provides evidence for the capacity of personalized high-resolution virtual brain models to investigate individual network effects in DBS for patients with TRD and opens up novel avenues in the personalized optimization of brain stimulation., Competing Interests: Declaration of Competing Interest The authors declare no competing interest., (Copyright © 2021. Published by Elsevier Inc.)

Understanding the neural underpinnings of major depressive disorder (MDD) and its treatment could improve treatment outcomes. So far, findings are variable and large sample replications scarce. We aimed to replicate and extend altered functional connectivity associated with MDD and pharmacotherapy outcomes in a large, multisite sample. Resting-state fMRI data were collected from 129 patients and 99 controls through the Canadian Biomarker Integration Network in Depression. Symptoms were assessed with the Montgomery-Åsberg Depression Rating Scale (MADRS). Connectivity was measured as correlations between four seeds (anterior and posterior cingulate cortex, insula and dorsolateral prefrontal cortex) and all other brain voxels. Partial least squares was used to compare connectivity prior to treatment between patients and controls, and between patients reaching remission (MADRS ≤ 10) early (within 8 weeks), late (within 16 weeks), or not at all. We replicated previous findings of altered connectivity in patients. In addition, baseline connectivity of the anterior/posterior cingulate and insula seeds differentiated patients with different treatment outcomes. The stability of these differences was established in the largest single-site subsample. Our replication and extension of altered connectivity highlighted previously reported and new differences between patients and controls, and revealed features that might predict remission prior to pharmacotherapy. Trial registration:ClinicalTrials.gov: NCT01655706., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Intra-individual transient temporal fluctuations in brain signal, as measured by fMRI blood oxygenation level dependent (BOLD) variability, is increasingly considered an important signal rather than measurement noise. Evidence from computational and cognitive neuroscience suggests that signal variability is a good proxy-measure of brain functional integrity and information processing capacity. Here, we sought to explore across-participant and longitudinal relationships between BOLD variability, age, and white matter structure in early childhood. We measured standard deviation of BOLD signal, total white matter volume, global fractional anisotropy (FA) and mean diffusivity (MD) during passive movie viewing in a sample of healthy children (aged 2-8 years; N = 83). We investigated how age and white matter development related to changes in BOLD variability both across- and within-participants. Our across-participant analyses using behavioural partial least squares (bPLS) revealed that the influence of age and white matter maturation on BOLD variability was highly interrelated. BOLD variability increased in widespread frontal, temporal and parietal regions, and decreased in the hippocampus and parahippocampal gyrus with age and white matter development. Our longitudinal analyses using linear mixed effects modelling revealed significant associations between BOLD variability, age and white matter microstructure. Analyses using artificial neural networks demonstrated that BOLD variability and white matter micro and macro-structure at earlier ages were strong predictors of BOLD variability at later ages. By characterizing the across-participant and longitudinal features of the association between BOLD variability and white matter micro- and macrostructure in early childhood, our results provide a novel perspective to understand structure-function relationships in the developing brain., (Copyright © 2021. Published by Elsevier Inc.)

Background: Unusual behavioral reactions to sensory stimuli are frequently reported in individuals on the autism spectrum (AS). Despite the early emergence of sensory features (< age 3) and their potential impact on development and quality of life, little is known about the neural mechanisms underlying sensory reactivity in early childhood autism., Methods: Here, we used electroencephalography (EEG) to investigate tactile cortical processing in young children aged 3-6 years with autism and in neurotypical (NT) children. Scalp EEG was recorded from 33 children with autism, including those with low cognitive and/or verbal abilities, and 45 age- and sex-matched NT children during passive tactile fingertip stimulation. We compared properties of early and later somatosensory-evoked potentials (SEPs) and their adaptation with repetitive stimulation between autistic and NT children and assessed whether these neural measures are linked to "real-world" parent-reported tactile reactivity., Results: As expected, we found elevated tactile reactivity in children on the autism spectrum. Our findings indicated no differences in amplitude or latency of early and mid-latency somatosensory-evoked potentials (P50, N80, P100), nor adaptation between autistic and NT children. However, latency of later processing of tactile information (N140) was shorter in young children with autism compared to NT children, suggesting faster processing speed in young autistic children. Further, correlational analyses and exploratory analyses using tactile reactivity as a grouping variable found that enhanced early neural responses were associated with greater tactile reactivity in autism., Limitations: The relatively small sample size and the inclusion of a broad range of autistic children (e.g., with low cognitive and/or verbal abilities) may have limited our power to detect subtle group differences and associations. Hence, replications are needed to verify these results., Conclusions: Our findings suggest that electrophysiological somatosensory cortex processing measures may be indices of "real-world" tactile reactivity in early childhood autism. Together, these findings advance our understanding of the neurophysiological mechanisms underlying tactile reactivity in early childhood autism and, in the clinical context, may have therapeutic implications.

Visual word recognition is a relatively effortless process, but recent research suggests the system involved is malleable, with evidence of increases in behavioural efficiency after prolonged lexical decision task (LDT) performance. However, the extent of neural changes has yet to be characterized in this context. The neural changes that occur could be related to a shift from initially effortful performance that is supported by control-related processing, to efficient task performance that is supported by domain-specific processing. To investigate this, we replicated the British Lexicon Project, and had participants complete 16 h of LDT over several days. We recorded electroencephalography (EEG) at three intervals to track neural change during LDT performance and assessed event-related potentials and brain signal complexity. We found that response times decreased during LDT performance, and there was evidence of neural change through N170, P200, N400, and late positive component (LPC) amplitudes across the EEG sessions, which suggested a shift from control-related to domain-specific processing. We also found widespread complexity decreases alongside localized increases, suggesting that processing became more efficient with specific increases in processing flexibility. Together, these findings suggest that neural processing becomes more efficient and optimized to support prolonged LDT performance.

It has been well documented that different types of nouns and action verbs are associated with behavioral and neural differences. In contrast, abstract verbs (e.g., think, dissolve) are often treated as a homogeneous category. We compared event-related potentials recorded during a syntactic classification task of four verb types; 1) abstract mental, 2) abstract emotional, 3) abstract nonbodily, and 4) concrete. Abstract nonbodily state verbs showed a sustained negativity at frontocentral electrodes and sustained positivity at parietal and occipital electrodes beginning 400 ms post-stimulus onset relative to abstract mental state and concrete verbs. Discrete source localization revealed a right inferior parietal source for all verbs and a distributed source estimation localized sources that distinguished between abstract mental state and abstract nonbodily state verbs to bilateral parietal cortex, left temporal cortex and right ventromedial prefrontal cortex. These findings suggest that different types of abstract verbs are associated with representational differences., (Copyright © 2020 Elsevier Inc. All rights reserved.)

To understand how the presence of stereoscopic disparity influences cognitive and neural processing, we recorded participants' behavior and scalp electrical activity while they performed a mental rotation task. Participants wore active shutter 3D goggles, allowing us to present stimuli with or without stereoscopic disparity on a trial-by-trial basis. Participants were more accurate and faster when stimuli were presented with stereoscopic disparity. This improvement in performance was accompanied by changes in neural activity recorded from scalp electrodes at parietal and occipital regions; stereoscopic disparity produced earlier P2 peaks, larger N2 amplitudes, and earlier, smaller P300 peak amplitudes. The presence of stereoscopic disparity also produced greater neural entropy at occipital electrode sites, and lower entropy at frontal sites. These findings suggest that the nature of the benefit afforded by stereoscopic disparity occurs at both low-level perceptual processing and higher-level cognitive processing, and results in more accurate and rapid performance., (Copyright © 2019 Elsevier Inc. All rights reserved.)

One of the strategies that researchers have used to investigate the role of sensorimotor information in lexical-semantic processing is to examine the effects of words' rated body-object interaction (BOI; i.e., the ease with which the human body can interact with a word's referent). Processing tends to be facilitated for words with high as compared with low BOI, across a wide variety of tasks. Such effects have been referenced in debates over the nature of semantic representations, but their theoretical import has been limited by the fact that BOI is a fairly coarse measure of sensorimotor experience with words' referents. In the present study, we collected ratings for 621 words on seven semantic dimensions (graspability, ease of pantomime, number of actions, animacy, size, danger, and usefulness), in order to investigate which attributes are most strongly related to BOI ratings and to lexical-semantic processing. BOI ratings were obtained from previous norming studies (Bennett, Burnett, Siakaluk, & Pexman in Behavior Research Methods, 43, 1100-1109, 2011; Tillotson, Siakaluk, & Pexman in Behavior Research Methods, 40, 1075-1078, 2008), and measures of lexical-semantic processing were obtained from previous behavioral megastudies involving either the semantic categorization task (concrete/abstract decision; Pexman, Heard, Lloyd, & Yap in Behavior Research Methods, 49, 407-417, 2017) or the lexical decision task (Balota et al., Behavior Research Methods, 39, 445-459, 2007). The results showed that the motor dimensions of graspability, ease of pantomime, and number of actions were all related to BOI, and that these dimensions together explained more variance in semantic processing than did the BOI ratings alone. These ratings will be useful for researchers who wish to study how different kinds of bodily interactions influence lexical-semantic processing and cognition.

Circumscribed interests are a symptom of autism spectrum disorder (ASD) that may be related to exaggerated affective neural responses. However, the use of generic ASD-interest image stimuli has left an open question as to whether affective responses towards individual interests are greater in ASD compared to typically developing (TD) controls. We compared amplitudes of the late positive potential (LPP), an affective electroencephalographic response, between adolescents with ASD (N = 19) and TD adolescents (N = 20), using images tailored to individual likes and dislikes. We found an LPP response for liked and disliked images, relative to neutral, with no difference in amplitude between groups. This suggests that the LPP is not atypical in adolescents with ASD towards images of individual interests.

Recent empirical work suggests that the dynamics of brain function, as measured by brain signal variability, differs between younger and older adults. We extended this work by examining how the relationship between brain signal variability and age is altered in the context of expertise. We recorded electroencephalography from Scrabble experts and controls during a visual word recognition task. To measure variability, we used multiscale entropy, which emphasizes the way brain signals behave over a range of timescales and can differentiate the variability of a complex system (the brain) from a purely random system. We replicated previously identified shifts from long-range interactions among neural populations to more local processing in late adulthood. In addition, we demonstrated an age-related increase in midrange neural interactions for experts, suggesting greater maintenance of network integration into late adulthood. Our results indicate that expertise-related differences in the context of age and brain dynamics occur across different timescales and that these differences are linked to task performance., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Objective: Neurocognitive functions are affected by high altitude, however the altitude effects of acclimatization and repeated exposures are unclear. We investigated the effects of acute, subacute and repeated exposure to 5,050 m on cognition among altitude-naïve participants compared to control subjects tested at low altitude. Methods: Twenty-one altitude-naïve individuals (25.3 ± 3.8 years, 13 females) were exposed to 5,050 m for 1 week ( Cycle 1) and re-exposed after a week of rest at sea-level ( Cycle 2 ). Baseline (BL, 520 m), acute (Day 1, HA1) and acclimatization (Day 6, HA6, 5,050 m) measurements were taken in both cycles. Seventeen control subjects (24.9 ± 2.6 years, 12 females) were tested over a similar period in Calgary, Canada (1,103 m). The Reaction Time (RTI), Attention Switching Task (AST), Rapid Visual Processing (RVP) and One Touch Stockings of Cambridge (OTS) tasks were administered and outcomes were expressed in milliseconds/frequencies. Lake Louise Score (LLS) and blood oxygen saturation (SpO 2 ) were recorded. Results: In both cycles, no significant changes were found with acute exposure on the AST total score, mean latency and SD. Significant changes were found upon acclimatization solely in the altitude group, with improved AST Mean Latency [HA1 (588 ± 92) vs. HA6 (526 ± 91), p < 0.001] and Latency SD [HA1 (189 ± 86) vs. HA6 (135 ± 65), p < 0.001] compared to acute exposure, in Cycle 1 . No significant differences were present in the control group. When entering Acute SpO 2 (HA1-BL), Acclimatization SpO 2 (HA6-BL) and LLS score as covariates for both cycles, the effects of acclimatization on AST outcomes disappeared indicating that the changes were partially explained by SpO 2 and LLS. The changes in AST Mean Latency [ΔBL (-61.2 ± 70.2) vs. ΔHA6 (-28.0 ± 58), p = 0.005] and the changes in Latency SD [ΔBL (-28.4 ± 41.2) vs. ΔHA6 (-0.2235 ± 34.8), p = 0.007] across the two cycles were smaller with acclimatization. However, the percent changes did not differ between cycles. These results indicate independent effects of altitude across repeated exposures. Conclusions: Selective and sustained attention are impaired at altitude and improves with acclimatization.The observed changes are associated, in part, with AMS score and SpO 2 . The gains in cognition with acclimatization during a first exposure are not carried over to repeated exposures.

Background: Previous work suggests that major depressive disorder (MDD) is associated with disturbances in global connectivity among brain regions, as well as local connectivity within regions. However, the relative importance of these global versus local changes for successful antidepressant treatment is unknown. We used multiscale entropy (MSE), a measure of brain signal variability, to examine how the propensity for local (fine scale MSE) versus global (coarse scale MSE) neural processing measured prior to antidepressant treatment is related to subsequent treatment response., Methods: We collected resting-state EEG activity during eyes-open and closed conditions from unmedicated individuals with MDD prior to antidepressant pharmacotherapy (N = 36) as well as from non-depressed controls (N = 36). Treatment response was assessed after 12 weeks of treatment using the Montgomery-Åsberg Depression Rating Scale (MADRS), at which time participants with MDD were characterized as either responders (≥ 50% MADRS decrease) or non-responders. MSE was calculated from baseline EEG, and compared between controls, future treatment responders and non-responders. Putative interactions with the well-documented age effect on signal variability (increased reliance on local neural communication with increasing age, indexed by greater finer-scale variability) were assessed., Results: Only in responders, we found that reduced MSE at fine temporal scales (especially fronto-centrally) and increased MSE diffusely at coarser temporal scales was related to the magnitude of the antidepressant response. In controls and MDD non-responders, but not MDD responders, there was an increase in MSE with age at fine temporal scales and a decrease in MSE with age at coarse temporal scales., Conclusion: Our results suggest that an increased propensity toward global processing, indexed by greater MSE at coarser timescales, at baseline appears to facilitate eventual antidepressant treatment response.

Increasing evidence suggests that brain signal variability is an important measure of brain function reflecting information processing capacity and functional integrity. In this study, we examined how maturation from childhood to adulthood affects the magnitude and spatial extent of state-to-state transitions in brain signal variability, and how this relates to cognitive performance. We looked at variability changes between resting-state and task (a symbol-matching task with three levels of difficulty), and within trial (fixation, post-stimulus, and post-response). We calculated variability with multiscale entropy (MSE), and additionally examined spectral power density (SPD) from electroencephalography (EEG) in children aged 8-14, and in adults aged 18-33. Our results suggest that maturation is characterized by increased local information processing (higher MSE at fine temporal scales) and decreased long-range interactions with other neural populations (lower MSE at coarse temporal scales). Children show MSE changes that are similar in magnitude, but greater in spatial extent when transitioning between internally- and externally-driven brain states. Additionally, we found that in children, greater changes in task difficulty were associated with greater magnitude of modulation in MSE. Our results suggest that the interplay between maturational and state-to-state changes in brain signal variability manifest across different spatial and temporal scales, and influence information processing capacity in the brain.

Previous studies have found that competitive Scrabble expertise is associated with enhanced performance on visual lexical decision tasks (LDT), particularly for vertically presented stimuli. In the current study, we investigated the underlying mechanisms responsible for this vertical fluency. We examined behaviour and neural activity during LDT in 19 competitive Scrabble players and 18 matched controls. Using event related potentials (ERP), we investigated whether Scrabble expertise modulates the N170, P300, and late positive component (LPC), associated with visual-orthographic processing, working memory, and stimulus classification, respectively. Behavioural results replicated those from previous studies: Scrabble experts were significantly faster than controls to respond to vertical stimuli in LDT. ERP results showed Scrabble experts had larger P300 amplitudes in right parietal electrodes compared to controls, as well as greater differentiation in LPC amplitudes between vertical words and nonwords. These findings suggest that the mechanism underlying vertical fluency in Scrabble experts involves enhanced domain-specific working memory and stimulus classification processes. The results have implications for understanding the flexibility of the adult visual word recognition system, as well as the behavioural and neural consequences of training within this system., (Copyright © 2017 Elsevier B.V. All rights reserved.)

We investigated transfer of the skills developed by competitive Scrabble players. Previous studies reported superior performance for Scrabble experts on the lexical decision task (LDT), suggesting near transfer of Scrabble skills. Here we investigated the potential for far transfer to a symbol decision task (SDT); in particular, transfer of enhanced long-term working memory for vertically presented stimuli. Our behavioral results showed no evidence for far transfer. Despite years of intensive practice, Scrabble experts were no faster and no more accurate than controls in the SDT. However, our fMRI and EEG data from the SDT suggest that the neural repertoire that Scrabble experts develop supports task performance even outside of the practiced domain, in a non-linguistic context. The regions engaged during the SDT were different across groups: controls engaged temporal-frontal regions, whereas Scrabble experts engaged posterior visual and temporal-parietal regions. In Scrabble experts, activity related to Scrabble skill (anagramming scores) included regions associated with visual-spatial processing and long-term working memory, and overlapped with regions previously shown to be associated with Scrabble expertise in the near transfer task (LDT). Analysis of source waveforms within these regions showed that participants with higher anagramming scores had larger P300 amplitudes, potentially reflecting greater working memory capacity, or less variability in the participants who performed the task more efficiently. Thus, the neuroimaging results provide evidence of brain transfer in the absence of behavioral transfer, providing new clues about the consequences of long-term training associated with competitive Scrabble expertise.

Recent empirical work suggests that, during healthy aging, the variability of network dynamics changes during task performance. Such variability appears to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into resting-state dynamics. We recorded EEG in young, middle-aged, and older adults during a "rest-task-rest" design and investigated if aging modifies the interaction between resting-state activity and external stimulus-induced activity. Using multiscale entropy as our measure of variability, we found that, with increasing age, resting-state dynamics shifts from distributed to more local neural processing, especially at posterior sources. In the young group, resting-state dynamics also changed from pre- to post-task, where fine-scale entropy increased in task-positive regions and coarse-scale entropy increased in the posterior cingulate, a key region associated with the default mode network. Lastly, pre- and post-task resting-state dynamics were linked to performance on the intervening task for all age groups, but this relationship became weaker with increasing age. Our results suggest that age-related changes in resting-state dynamics occur across different spatial and temporal scales and have consequences for information processing capacity.

Competitive Scrabble players devote considerable time to studying words and practicing Scrabble-related skills (e.g., anagramming). This training is associated with extraordinary performance in lexical decision, the standard visual word recognition task (Hargreaves, Pexman, Zdrazilova & Sargious, 2012). In the present study we investigated the neural consequences of this lexical expertise. Using both event-related and resting-state fMRI, we compared brain activity and connectivity in 12 competitive Scrabble experts with 12 matched non-expert controls. Results showed that when engaged in the lexical decision task (LDT), Scrabble experts made use of brain regions not generally associated with meaning retrieval in visual word recognition, but rather those associated with working memory and visual perception. The analysis of resting-state data also showed group differences, such that a different network of brain regions was associated with higher levels of Scrabble-related skill in experts than in controls., (Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This study investigated the effects of a high-intensity cycling exercise on changes in spectral and temporal aspects of electroencephalography (EEG) measured from 10 experienced cyclists. Cyclists performed a maximum aerobic power test on the first testing day followed by a time-to-exhaustion trial at 85% of their maximum power output on 2 subsequent days that were separated by ∼48 h. EEG was recorded using a 64-channel system at 500 Hz. Independent component (IC) analysis parsed the EEG scalp data into maximal ICs. An equivalent current dipole model was calculated for each IC, and results were clustered across subjects. A time-frequency analysis of the identified electrocortical clusters was performed to investigate the magnitude and timing of event-related spectral perturbations. Significant changes (P < 0.05) in electrocortical activity were found in frontal, supplementary motor and parietal areas of the cortex. Overall, there was a significant increase in EEG power as fatigue developed throughout the exercise. The strongest increase was found in the frontal area of the cortex. The timing of event-related desynchronization within the supplementary motor area corresponds with the onset of force production and the transition from flexion to extension in the pedaling cycle. The results indicate an involvement of the cerebral cortex during the pedaling task that most likely involves executive control function, as well as motor planning and execution., (Copyright © 2016 the American Physiological Society.)

Recent theoretical and empirical work has focused on the variability of network dynamics in maturation. Such variability seems to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into healthy aging. Two different data sets, one EEG (total n = 48, ages 18-72) and one magnetoencephalography (n = 31, ages 20-75) were analyzed for such spatiotemporal dependency using multiscale entropy (MSE) from regional brain sources. In both data sets, the changes in MSE were timescale dependent, with higher entropy at fine scales and lower at more coarse scales with greater age. The signals were parsed further into local entropy, related to information processed within a regional source, and distributed entropy (information shared between two sources, i.e., functional connectivity). Local entropy increased for most regions, whereas the dominant change in distributed entropy was age-related reductions across hemispheres. These data further the understanding of changes in brain signal variability across the lifespan, suggesting an inverted U-shaped curve, but with an important qualifier. Unlike earlier in maturation, where the changes are more widespread, changes in adulthood show strong spatiotemporal dependence., (© The Authors 2013. Published by Oxford University Press.)

Computational models predict that focal damage to the Default Mode Network (DMN) causes widespread decreases and increases of functional DMN connectivity. How such alterations impact functioning in a specific cognitive domain such as episodic memory remains relatively unexplored. Here, we show in patients with unilateral medial temporal lobe epilepsy (mTLE) that focal structural damage leads indeed to specific patterns of DMN functional connectivity alterations, specifically decreased connectivity between both medial temporal lobes (MTLs) and the posterior part of the DMN and increased intrahemispheric anterior-posterior connectivity. Importantly, these patterns were associated with better and worse episodic memory capacity, respectively. These distinct patterns, shown here for the first time, suggest that a close dialogue between both MTLs and the posterior components of the DMN is required to fully express the extensive repertoire of episodic memory abilities.

Recently, deep brain stimulation (DBS) has been evaluated as an experimental therapy for treatment-resistant depression. Although there have been encouraging results in open-label trials, about half of the patients fail to achieve meaningful benefit. Although progress has been made in understanding the neurobiology of MDD, the ability to characterize differences in brain dynamics between those who do and do not benefit from DBS is lacking. In this study, we investigated EEG resting-state data recorded from 12 patients that have undergone DBS surgery. Of those, six patients were classified as responders to DBS, defined as an improvement of 50% or more on the 17-item Hamilton Rating Scale for Depression (HAMD-17). We compared hemispheric frontal theta and parietal alpha power asymmetry and synchronization asymmetry between responders and non-responders. Hemispheric power asymmetry showed statistically significant differences between responders and non-responders with healthy controls showing an asymmetry similar to responders but opposite to non-responders. This asymmetry was characterized by an increase in frontal theta in the right hemisphere relative to the left combined with an increase in parietal alpha in the left hemisphere relative to the right in non-responders compared with responders. Hemispheric mean synchronization asymmetry showed a statistically significant difference between responders and non-responders in the theta band, with healthy controls showing an asymmetry similar to responders but opposite to non-responders. This asymmetry resulted from an increase in frontal synchronization in the right hemisphere relative to the left combined with an increase in parietal synchronization in the left hemisphere relative to the right in non-responders compared with responders. Connectivity diagrams revealed long-range differences in frontal/central-parietal connectivity between the two groups in the theta band. This pattern was observed irrespective of whether EEG data were collected with active DBS or with the DBS stimulation turned off, suggesting stable functional and possibly structural modifications that may be attributed to plasticity.

Spatial orientation is a complex cognitive process requiring the integration of information processed in a distributed system of brain regions. Current models on the neural basis of spatial orientation are based primarily on the functional role of single brain regions, with limited understanding of how interaction among these brain regions relates to behavior. In this study, we investigated two sources of variability in the neural networks that support spatial orientation--network configuration and efficiency--and assessed whether variability in these topological properties relates to individual differences in orientation accuracy. Participants with higher accuracy were shown to express greater activity in the right supramarginal gyrus, the right precentral cortex, and the left hippocampus, over and above a core network engaged by the whole group. Additionally, high-performing individuals had increased levels of global efficiency within a resting-state network composed of brain regions engaged during orientation and increased levels of node centrality in the right supramarginal gyrus, the right primary motor cortex, and the left hippocampus. These results indicate that individual differences in the configuration of task-related networks and their efficiency measured at rest relate to the ability to spatially orient. Our findings advance systems neuroscience models of orientation and navigation by providing insight into the role of functional integration in shaping orientation behavior.

Computational modeling suggests that variability in brain signals provides important information regarding the system's capacity to adopt different network configurations that may promote optimal responding to stimuli. Although there is limited empirical work on this construct, a recent study indicates that age-related decreases in variability across the adult lifespan correlate with less efficient and less accurate performance. Here, we extend this construct to the assessment of cerebral integrity by comparing fMRI BOLD variability and fMRI BOLD amplitude in their ability to account for differences in functional capacity in patients with focal unilateral medial temporal dysfunction. We were specifically interested in whether either of these BOLD measures could identify a link between the affected medial temporal region and memory performance (as measured by a clinical test of verbal memory retention). Using partial least-squares analyses, we found that variability in a set of regions including the left hippocampus predicted verbal retention and, furthermore, this relationship was similar across a range of cognitive tasks measured during scanning (i.e., the same pattern was seen in fixation, autobiographical recall, and word generation). In contrast, signal amplitude in the hippocampus did not predict memory performance, even for a task that reliably activates the medial temporal lobes (i.e., autobiographical recall). These findings provide a powerful validation of the concept that variability in brain signals reflects functional integrity. Furthermore, this measure can be characterized as a robust biomarker in this clinical setting because it reveals the same pattern regardless of cognitive challenge or task engagement during scanning.

Traditionally, the medial temporal lobe (MTL) is thought to be dedicated to declarative memory. Recent evidence challenges this view, suggesting that perirhinal cortex (PrC), which interfaces the MTL with the ventral visual pathway, supports highly integrated object representations in recognition memory and perceptual discrimination. Even with comparable representational demands, perceptual and memory tasks differ in numerous task demands and the subjective experience they evoke. Here, we tested whether such differences are reflected in distinct patterns of connectivity between PrC and other cortical regions, including differential involvement of prefrontal control processes. We examined functional magnetic resonance imaging data for closely matched perceptual and recognition memory tasks for faces that engaged right PrC equivalently. Multivariate seed analyses revealed distinct patterns of interactions: Right ventrolateral prefrontal and posterior cingulate cortices exhibited stronger functional connectivity with PrC in recognition memory; fusiform regions were part of the pattern that displayed stronger functional connectivity with PrC in perceptual discrimination. Structural equation modeling revealed distinct patterns of effective connectivity that allowed us to constrain interpretation of these findings. Overall, they demonstrate that, even when MTL structures show similar involvement in recognition memory and perceptual discrimination, differential neural mechanisms are reflected in the interplay between the MTL and other cortical regions.

Although the hippocampus is not considered a key structure in semantic memory, patients with medial-temporal lobe epilepsy (mTLE) have deficits in semantic access on some word retrieval tasks. We hypothesized that these deficits reflect the negative impact of focal epilepsy on remote cerebral structures. Thus, we expected that the networks that support word retrieval tasks would be altered in left mTLE patients. We measured brain activity with fMRI while participants (13 controls, 13 left mTLE, and 13 right mTLE) performed a verb generation task. We examined functional connectivity during this task in relation to language performance on an off-line clinical test of lexical access (Boston Naming Test, BNT). Using task-seed-behavior partial least squares, we identified a canonical language network that was more active during verb generation than the baseline condition, but this network did not correlate with variability in BNT performance in either controls or patients. Instead, additional networks were identified for each group, with more anterior temporal and prefrontal regions recruited for controls and more posterior temporal regions for both left and right mTLE patients. Our findings go beyond the literature emphasizing differences in laterality of language processes in mTLE patients and, critically, highlight how network changes can be used to account for performance variation among patients on clinically relevant measures. This strategy of correlating network changes and off-line behavior may provide a powerful tool for predicting a postoperative decline in language performance.

Selective dysfunction in the medial temporal lobe (MTL) in amnestic mild cognitive impairment (MCI) results in a relatively circumscribed impairment in episodic memory. Previously, we found that activation extent in MTL during encoding correlated with subsequent recognition (hit rate) in controls but not in MCI patients (Mandzia et al. [2009]: Neurobiol Aging 30:717-730). Here, we examined whether functional connectivity amongst MTL and cortical regions might better explain differences in subsequent recognition success. Participants underwent fMRI scanning during picture encoding, and multivariate analysis was used to characterize the relationship between network activations and recognition. Both patients and controls activated a canonical MTL encoding network. However, this network correlated with hit rate only for controls. In MCI patients, recognition variability was best explained by the engagement of an additional network including BA 20. We propose that this pattern represents functional reorganization caused by reduced efficiency in the MTL network. Our findings suggest that understanding brain-behavior relationships in neurological disorders requires examination of large-scale networks, even when dysfunction is relatively focal as in MCI., (Copyright © 2010 Wiley-Liss, Inc.)

Encoding and retrieval of relational information requires interaction between the hippocampus and various neocortical regions, but it is unknown whether the connectivity of hippocampal-neocortical networks is different at input and output stages. To examine this, we conducted a network analysis of event-related fMRI data collected during a face-recognition, remember/know paradigm. Directed analyses in the medial temporal lobe identified a small region in the left hippocampus that showed differential activation for encoding and retrieval of recollected versus familiar items. Multivariate seed partial least squares (PLS) analysis was used to identify brain regions that were functionally connected to this hippocampal region at encoding and retrieval of 'remembered' items. Anatomically based structural equation modeling (SEM) was then used to test for differences in effective connectivity of network nodes between these two memory stages. The SEM analysis revealed a reversal of directionality between the left hippocampus (LHC) and left inferior parietal cortex (LIPC) at encoding and retrieval. During encoding, activation of the LHC had a positive influence on the LIPC, whereas during retrieval the reverse pattern was found, i.e., the LIPC activation positively influenced LHC activation. These findings emphasize the importance of hippocampal-parietal connections and underscore the complexity of their interactions in initial binding and retrieval/reintegration of relational memory. We also found that, during encoding, the right hippocampus had a positive influence on the right retrospenial cortex, whereas during retrieval this influence was significantly weaker. We submit that examining patterns of connectivity can be important both to elaborate and constrain models of memory involving hippocampal-neocortical interactions., (Copyright 2010 Elsevier Ltd. All rights reserved.)

Patients with mesial temporal lobe epilepsy (mTLE) show structural and functional abnormalities in hippocampus and surrounding mesial temporal structures. Brain signal complexity appears to be a marker of functional integrity or capacity. We examined complexity in 8 patients with intracranial hippocampal electrodes during performance of memory tasks (scene encoding and recognition) known to be sensitive to mesial temporal integrity. Our patients were shown to have right mesial temporal seizure onsets, permitting us to evaluate both epileptogenic (right) and healthy (left) hippocampi. Using multiscale entropy (MSE) as a measure of complexity, we found that iEEG from the epileptogenic hippocampus showed less complexity than iEEG from the healthy hippocampus. This difference was reliable for encoding but not for recognition. Our results indicate that both functional integrity and cognitive demands influence hippocampal signal complexity.

We explored the effects of aging on 2 large-scale brain networks, the default mode network (DMN) and the task-positive network (TPN). During functional magnetic resonance imaging scanning, young and older participants carried out 4 visual tasks: detection, perceptual matching, attentional cueing, and working memory. Accuracy of performance was roughly matched at 80% across tasks and groups. Modulations of activity across conditions were assessed, as well as functional connectivity of both networks. Younger adults showed a broader engagement of the DMN and older adults a more extensive engagement of the TPN. Functional connectivity in the DMN was reduced in older adults, whereas the main pattern of TPN connectivity was equivalent in the 2 groups. Age-specific connectivity also was seen in TPN regions. Increased activity in TPN areas predicted worse accuracy on the tasks, but greater expression of a connectivity pattern associated with a right dorsolateral prefrontal TPN region, seen only in older adults, predicted better performance. These results provide further evidence for age-related differences in the DMN and new evidence of age differences in the TPN. Increased use of the TPN may reflect greater demand on cognitive control processes in older individuals that may be partially offset by alterations in prefrontal functional connectivity.

Several theories of brain function emphasize distinctions between sensory and cognitive systems. We hypothesized, instead, that sensory and cognitive systems interact to instantiate the task at the neural level. We tested whether input modality interacts with working memory operations in that, despite similar cognitive demands, differences in the anatomical locations or temporal dynamics of activations following auditory or visual input would not be limited to the sensory cortices. We recorded event-related brain potentials (ERPs) while participants performed simple short-term memory tasks involving visually or auditorily presented bandpass-filtered noise stimuli. Our analyses suggested that working memory operations in each modality had a very similar spatial distribution of current sources outside the sensory cortices, but differed in terms of time course. Specifically, information for visual processing was updated and held online in a manner that was different from auditory processing, which was done mostly after the offset of the final stimulus. Our results suggest that the neural networks that support working memory operations have different temporal dynamics for auditory and visual material, even when the stimuli are matched in term of discriminability, and are designed to undergo very similar transformations when they are encoded and retrieved from memory.

Emerging ideas of brain function emphasize the context-dependency of regional contributions to cognitive operations, where the function of a particular region is constrained by its pattern of functional connectivity. We used functional magnetic resonance imaging to examine how modality of input (auditory or visual) affects prefrontal cortex (PFC) functional connectivity for simple working memory tasks. The hypothesis was that PFC would show contextually dependent changes in functional connectivity in relation to the modality of input despite similar cognitive demands. Participants were presented with auditory or visual bandpass-filtered noise stimuli, and performed 2 simple short-term memory tasks. Brain activation patterns independently mapped onto modality and task demands. Analysis of right ventral PFC functional connectivity, however, suggested these activity patterns interact. One functional connectivity pattern showed task differences independent of stimulus modality and involved ventromedial and dorsolateral prefrontal and occipitoparietal cortices. A second pattern showed task differences that varied with modality, engaging superior temporal and occipital association regions. Importantly, these association regions showed nonzero functional connectivity in all conditions, rather than showing a zero connectivity in one modality and nonzero in the other. These results underscore the interactive nature of brain processing, where modality-specific and process-specific networks interact for normal cognitive operations.

We used functional magnetic resonance imaging to examine how modality of input affects functional network organization beyond the sensory cortices for simple working memory tasks. The stimuli were auditory or visual bandpass-filtered white noise. On a given trial, three stimuli, each with differing center frequencies, were presented in succession. For temporal sequencing tasks, participants indicated when the stimulus with the highest frequency content appeared. For comparison tasks, participants indicated whether the frequency content of the last stimulus was lower, intermediate, or higher than the first two stimuli. Task difficulty was equated by establishing equivalent accuracy thresholds across subjects. We used behavioral spatiotemporal partial-least squares (ST-bPLS) analysis to identify neural patterns capturing the optimal association between brain images and reaction time. Because of statistical instabilities, subjects were divided into a SLOW group and a FAST group based on the median split of reaction times. ST-bPLS identified a significant interaction between stimulus modality and task demands for both groups, indicating that task-dependent brain-behavior correlations changed with stimulus modality. The large-scale activity pattern associated with this effect included prefrontal cortex and parietal cortex for the SLOW group and parietal cortex and cingulate for the FAST group. For the FAST group only, ST-bPLS also identified a significant main effect that differentiated tasks independent of modality. The pattern associated with this effect included prefrontal cortex and parietal cortex. These results confirm that modality of input affects network configuration even outside of the sensory cortices but that network configuration may vary with behavior.

Structural equation modeling (SEM) is a statistical method that can assess changes in effective connectivity across tasks or between groups. In its initial application to neuroimaging data, anatomical connectivity provided the constraints to decompose interregional covariances to estimate effective connections. There have been concerns expressed, however, with the validity of interpreting effective connections for a model that does not adequately fit the data. We sought to address this concern by creating two population networks with different patterns of effective connectivity, extracting three samples sizes (N = 100, 60, 20), and then assessing whether the ability to detect effective connectivity differences depended on absolute model fit. Four scenarios were assessed: (1) elimination of a region showing no task differences; (2) elimination of connections with no task differences; (3) elimination of connections that carried task differences, but could be expressed through alternative indirect routes; (4) elimination of connections that carried task differences, and could not be expressed through indirect routes. We were able to detect task differences in all four cases, despite poor absolute model fit. In scenario 3, total effects captured the overall task differences even though the direct effect was no longer present. In scenario 4, task differences that were included in the model remained, but the missing effect was not expressed. In conclusion, it seems that when independent information (e.g., anatomical connectivity) is used to define the causal structure in SEM, inferences about task- or group-dependent changes are valid regardless of absolute model fit., ((c) 2006 Wiley-Liss, Inc.)

Partial least squares (PLS) has proven to be a important multivariate analytic tool for positron emission tomographic and, more recently, event-related potential (ERP) data. The application to ERP incorporates the ability to analyze space and time together, a feature that has obvious appeal for event-related functional magnetic resonance imaging (fMRI) data. This paper presents the extension of spatiotemporal PLS (ST-PLS) to fMRI, explaining the theoretical foundation and application to an fMRI study of auditory and visual perceptual memory. Analysis of activation effects with ST-PLS was compared with conventional univariate random effects analysis, showing general consensus for both methods, but several unique observations by ST-PLS, including enhanced statistical power. The application of ST-PLS for assessment of task-dependent brain-behavior relationships is also presented. Singular features of ST-PLS include (1) no assumptions about the shape of the hemodynamic response functions (HRFs); (2) robust statistical assessment at the image level through permutation tests; (3) protection against outlier influences at the voxel level through bootstrap resampling; (4) flexible analytic configurations that allow assessment of activation difference, brain-behavior relations, and functional connectivity. These features enable ST-PLS to act as an important complement to other multivariate and univariate approaches used in neuroimaging research.