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Network analysis can provide insight into key organizational principles of brain structure and help identify structural changes associated with brain disease. Though static differences between diseased and healthy networks are well characterized, the study of network dynamics, or how brain networks change over time, is increasingly central to understanding ongoing brain changes throughout disease. Accordingly, we present a short review of network models of spread, network dynamics, and network degeneration. Borrowing from recent suggestions, we divide this review into two processes by which brain networks can change: dynamics on networks, which are functional and pathological consequences taking place atop a static structural brain network; and dynamics of networks, which constitutes a changing structural brain network. We focus on diffusion magnetic resonance imaging-based structural or anatomic connectivity graphs. We address psychiatric disorders like schizophrenia; developmental disorders like epilepsy; stroke; and Alzheimer's disease and other neurodegenerative diseases.

Cognitive impairment is a feature of many psychiatric diseases, including schizophrenia. Here we aim to identify multimodal biomarkers for quantifying and predicting cognitive performance in individuals with schizophrenia and healthy controls. A supervised learning strategy is used to guide three-way multimodal magnetic resonance imaging (MRI) fusion in two independent cohorts including both healthy individuals and individuals with schizophrenia using multiple cognitive domain scores. Results highlight the salience network (gray matter, GM), corpus callosum (fractional anisotropy, FA), central executive and default-mode networks (fractional amplitude of low-frequency fluctuation, fALFF) as modality-specific biomarkers of generalized cognition. FALFF features are found to be more sensitive to cognitive domain differences, while the salience network in GM and corpus callosum in FA are highly consistent and predictive of multiple cognitive domains. These modality-specific brain regions define-in three separate cohorts-promising co-varying multimodal signatures that can be used as predictors of multi-domain cognition.

Environmental cues, through Pavlovian learning, become conditioned stimuli that guide animals toward the acquisition of rewards (for example, food) that are necessary for survival. We tested the fundamental role of midbrain dopamine neurons in conferring predictive and motivational properties to cues, independent of external rewards. We found that brief phasic optogenetic excitation of dopamine neurons, when presented in temporal association with discrete sensory cues, was sufficient to instantiate those cues as conditioned stimuli that subsequently both evoked dopamine neuron activity on their own and elicited cue-locked conditioned behavior. Notably, we identified highly parcellated functions for dopamine neuron subpopulations projecting to different regions of striatum, revealing dissociable dopamine systems for the generation of incentive value and conditioned movement invigoration. Our results indicate that dopamine neurons orchestrate Pavlovian conditioning via functionally heterogeneous, circuit-specific motivational signals to create, gate, and shape cue-controlled behaviors.

OBJECTIVE:Functions ascribed to the hippocampal sub-regions for encoding episodic memories include the separation of activity patterns propagated from the entorhinal cortex (EC) into the dentate gyrus (DG) and pattern completion in CA3 region. Since a direct assessment of these functions is lacking at the level of specific axonal inputs, our goal is to directly measure the separation and completion of distinct axonal inputs in engineered pairs of hippocampal sub-regional circuits. APPROACH:We co-cultured EC-DG, DG-CA3, CA3-CA1 or CA1-EC neurons in a two-chamber PDMS device over a micro-electrode array (MEA60), inter-connected via distinct axons that grow through the micro-tunnels between the compartments. Taking advantage of the axonal accessibility, we quantified pattern separation and completion of the evoked activity transmitted through the tunnels from source into target well. Since pattern separation can be inferred when inputs are more correlated than outputs, we first compared the correlations among axonal inputs with those of target somata outputs. We then compared, in an analog approach, the distributions of correlation distances between rate patterns of the axonal inputs inside the tunnels with those of the somata outputs evoked in the target well. Finally, in a digital approach, we measured the spatial population distances between binary patterns of the same axonal inputs and somata outputs. MAIN RESULTS:We found the strongest separation of the propagated axonal inputs when EC was axonally connected to DG, with a decline in separation to CA3 and to CA1 for both rate and digital approaches. Furthermore, the digital approach showed stronger pattern completion in CA3, then CA1 and EC. SIGNIFICANCE:To the best of our knowledge, these are the first direct measures of pattern separation and completion for axonal transmission to the somata target outputs at the rate and digital population levels in each of four stages of the EC-DG-CA3-CA1 circuit.

Diverse genetic and environmental etiologies converge onto circuit level brain dysfunction in autism spectrum disorder (ASD), manifesting at a macroscopic level as aberrant neural connectivity. Previous studies have described atypical patterns of decreased short range and increased long range connectivity in ASD [1]. However, it remains unclear whether group level features of circuit dysfunction are consistently present across the range of cognitive function seen in the autism spectrum. The dynamics of neural oscillations in the alpha range (6-12 Hz) are exquisitely sensitive to healthy development of functional and structural connectivity. Alpha-band coherence, measured with high temporal-precision electroencephalography (EEG) therefore represents an ideal tool for studying neural connectivity in developmental populations. Here we examined spontaneous alpha phase coherence in a heterogeneous sample of 59 children with ASD and 39 age matched typically developing children. Using a data driven approach, we conducted an unbiased examination of all possible atypical connectivity patterns across all cortical regions. Long-range hypoconnectivity was present in children with ASD compared to typically developing children, with temporal interhemispheric connectivity showing the largest difference between the two groups. Decreased long range alpha coherence distinguishes a heterogeneous group of ASD children from typically developing children. Interhemispheric temporal hypoconnectivity represents a fundamental functional difference in children with ASD across a wide cognitive and age range that may reflect white matter disturbances or increased signal variability at temporal sites in ASD.

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (electroencephalograms, magnetoencephalograms, electrocorticograms and local field potentials) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide recommendations for interpreting the data using forward and inverse models. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems.

Chemical exposures have been implicated as environmental risk factors that interact with genetic susceptibilities to influence individual risk for complex neurodevelopmental disorders, including autism spectrum disorder, schizophrenia, attention deficit hyperactivity disorder and intellectual disabilities. Altered patterns of neuronal connectivity represent a convergent mechanism of pathogenesis for these and other neurodevelopmental disorders, and growing evidence suggests that chemicals can interfere with specific signaling pathways that regulate the development of neuronal connections. There is, therefore, a growing interest in developing screening platforms to identify chemicals that alter neuronal connectivity. Cell-cell, cell-matrix interactions and systemic influences are known to be important in defining neuronal connectivity in the developing brain, thus, a systems-based model offers significant advantages over cell-based models for screening chemicals for effects on neuronal connectivity. The embryonic zebrafish represents a vertebrate model amenable to higher throughput chemical screening that has proven useful in characterizing conserved mechanisms of neurodevelopment. Moreover, the zebrafish is readily amenable to gene editing to integrate genetic susceptibilities. Although use of the zebrafish model in toxicity testing has increased in recent years, the diverse tools available for imaging structural differences in the developing zebrafish brain have not been widely applied to studies of the influence of gene by environment interactions on neuronal connectivity in the developing zebrafish brain. Here, we discuss tools available for imaging of neuronal connectivity in the developing zebrafish, review what has been published in this regard, and suggest a path forward for applying this information to developmental neurotoxicity testing.

Historically, research on the cognitive processes that support human memory proceeded, to a large extent, independently of research on the neural basis of memory. Accumulating evidence from neuroimaging, however, has enabled the field to develop a broader and more integrative perspective. Here, we briefly outline how advances in cognitive neuroscience can potentially shed light on concepts and controversies in human memory research. We argue that research on the functional properties of cortico-hippocampal networks informs us about how memories might be organized in the brain, which, in turn, helps to reconcile seemingly disparate perspectives in cognitive psychology. Finally, we discuss several open questions and directions for future research.

Adolescence is a complex period of concurrent mental and physical development that facilitates adult functioning at multiple levels. Despite the growing number of neuroimaging studies of cognitive development in adolescence focusing on regional activation patterns, there remains a paucity of information about the functional interactions across these participating regions that are critical for cognitive functioning, including memory. The current study used structural equation modeling (SEM) to determine how interactions among brain regions critical for memory change over the course of adolescence. We obtained functional MRI in 77 individuals aged 8-16 years old, divided into younger (ages 8-10) and older (ages > 11) cohorts, using an incidental encoding memory task to activate hippocampus formation and associated brain networks, as well as behavioral data on memory function. SEM was performed on the imaging data for four groups (younger girls, younger boys, older girls, and older boys) that were subsequently compared using a stacked model approach. Significant differences were seen between the models for these groups. Younger boys had a predominantly posterior distribution of connections originating in primary visual regions and terminating on multi-modal processing regions. In older boys, there was a relatively greater anterior connection distribution, with increased effective connectivity within association and multi-modal processing regions. Connection patterns in younger girls were similar to those of older boys, with a generally anterior-posterior distributed network among sensory, multi-modal, and limbic regions. In contrast, connections in older girls were widely distributed but relatively weaker. Memory performance increased with age, without a significant difference between the sexes. These findings suggest a progressive reorganization among brain regions, with a commensurate increase in efficiency of cognitive functioning, from younger to older individuals in both girls and boys, providing insight into the age- and gender-specific processes at play during this critical transition period.

Resting- or baseline-state low-frequency (0.01-0.2 Hz) brain activity is observed in fMRI, EEG, and local field potential recordings. These fluctuations were found to be correlated across brain regions and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow resting-state fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (

The neural mechanism for selectively tuning in to a target speaker while tuning out the others in a multi-speaker situation (i.e., the cocktail-party effect) remains elusive. Here we addressed this issue by measuring brain activity simultaneously from a listener and from multiple speakers while they were involved in naturalistic conversations. Results consistently show selectively enhanced interpersonal neural synchronization (INS) between the listener and the attended speaker at left temporal-parietal junction, compared with that between the listener and the unattended speaker across different multi-speaker situations. Moreover, INS increases significantly prior to the occurrence of verbal responses, and even when the listener's brain activity precedes that of the speaker. The INS increase is independent of brain-to-speech synchronization in both the anatomical location and frequency range. These findings suggest that INS underlies the selective process in a multi-speaker situation through neural predictions at the content level but not the sensory level of speech.

Memories link information about specific experiences to more general knowledge that is abstracted from and contextualizes those experiences. Hippocampal-cortical activity patterns representing features of past experience are reinstated during awake memory reactivation events, but whether representations of both specific and general features of experience are simultaneously reinstated remains unknown. We examined hippocampal and prefrontal cortical firing patterns during memory reactivation in rats performing a well-learned foraging task with multiple spatial paths. We found that specific hippocampal place representations are preferentially reactivated with the subset of prefrontal cortical task representations that generalize across different paths. Our results suggest that hippocampal-cortical networks maintain links between stored representations for specific and general features of experience, which could support abstraction and task guidance in mammals.

Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. PBE activity has historically been analyzed as a phenomenon subordinate to the place code. Here, we use hidden Markov models to study PBEs observed in rats during exploration of both linear mazes and open fields. We demonstrate that estimated models are consistent with a spatial map of the environment, and can even decode animals' positions during behavior. Moreover, we demonstrate the model can be used to identify hippocampal replay without recourse to the place code, using only PBE model congruence. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Additionally, by forming models independent of animal behavior, we lay the groundwork for studies of non-spatial memory.

Auditory and visual speech information are often strongly integrated resulting in perceptual enhancements for audiovisual (AV) speech over audio alone and sometimes yielding compelling illusory fusion percepts when AV cues are mismatched, the McGurk-MacDonald effect. Previous research has identified three candidate regions thought to be critical for AV speech integration: the posterior superior temporal sulcus (STS), early auditory cortex, and the posterior inferior frontal gyrus. We assess the causal involvement of these regions (and others) in the first large-scale (N = 100) lesion-based study of AV speech integration. Two primary findings emerged. First, behavioral performance and lesion maps for AV enhancement and illusory fusion measures indicate that classic metrics of AV speech integration are not necessarily measuring the same process. Second, lesions involving superior temporal auditory, lateral occipital visual, and multisensory zones in the STS are the most disruptive to AV speech integration. Further, when AV speech integration fails, the nature of the failure-auditory vs visual capture-can be predicted from the location of the lesions. These findings show that AV speech processing is supported by unimodal auditory and visual cortices as well as multimodal regions such as the STS at their boundary. Motor related frontal regions do not appear to play a role in AV speech integration.

Connectivity in intrinsically connected networks (ICNs) may predict individual differences in cognition and behavior. The drastic alterations in socioemotional awareness of patients with behavioral variant frontotemporal dementia (bvFTD) are presumed to arise from changes in one such ICN, the salience network (SN). We examined how individual differences in SN connectivity are reflected in overt social behavior in healthy individuals and patients, both to provide neuroscientific insight into this key brain-behavior relationship, and to provide a practical tool to diagnose patients with early bvFTD. We measured SN functional connectivity and socioemotional sensitivity in 65 healthy older adults and 103 patients in the earliest stage [Clinical Dementia Rating (CDR) Scale score ≤1] of five neurodegenerative diseases [14 bvFTD, 29 Alzheimer's disease (AD), 20 progressive supranuclear palsy (PSP), 21 semantic variant primary progressive aphasia (svPPA), and 19 non-fluent variant primary progressive aphasia (nfvPPA)]. All participants underwent resting-state functional imaging and an informant described their responsiveness to subtle emotional expressions using the Revised Self-Monitoring Scale (RSMS). Higher functional connectivity in the SN, predominantly between the right anterior insula (AI) and both "hub" cortical and "interoceptive" subcortical nodes, predicted socioemotional sensitivity among healthy individuals, showing that socioemotional sensitivity is a behavioral marker of SN function, and particularly of right AI functional connectivity. The continuity of this relationship in both healthy and neurologically affected individuals highlights the role of socioemotional sensitivity as an early diagnostic marker of SN connectivity. Clinically, this is particularly important for identification of patients in the earliest stage of bvFTD, where the SN is selectively vulnerable.

Understanding brain function requires technologies that can control the activity of large populations of neurons with high fidelity in space and time. We developed a multiphoton holographic approach to activate or suppress the activity of ensembles of cortical neurons with cellular resolution and sub-millisecond precision. Since existing opsins were inadequate, we engineered new soma-targeted (ST) optogenetic tools, ST-ChroME and IRES-ST-eGtACR1, optimized for multiphoton activation and suppression. Employing a three-dimensional all-optical read-write interface, we demonstrate the ability to simultaneously photostimulate up to 50 neurons distributed in three dimensions in a 550 × 550 × 100-µm3 volume of brain tissue. This approach allows the synthesis and editing of complex neural activity patterns needed to gain insight into the principles of neural codes.

Persistent neural activity, the substrate of working memory, is thought to emerge from synaptic reverberation within recurrent networks. However, reverberation models do not robustly explain the fundamental dynamics of persistent activity, including high-spiking irregularity, large intertrial variability, and state transitions. While cellular bistability may contribute to persistent activity, its rigidity appears incompatible with persistent activity labile characteristics. Here, we unravel in a cellular model a form of spike-mediated conditional bistability that is robust and generic. and provides a rich repertoire of mnemonic computations. Under asynchronous synaptic inputs of the awakened state, conditional bistability generates spiking/bursting episodes, accounting for the irregularity, variability, and state transitions characterizing persistent activity. This mechanism has likely been overlooked because of the subthreshold input it requires, and we predict how to assess it experimentally. Our results suggest a reexamination of the role of intrinsic properties in the collective network dynamics responsible for flexible working memory.SIGNIFICANCE STATEMENT This study unravels a novel form of intrinsic neuronal property: conditional bistability. We show that, thanks to its conditional character, conditional bistability favors the emergence of flexible and robust forms of persistent activity in PFC neural networks, in opposition to previously studied classical forms of absolute bistability. Specifically, we demonstrate for the first time that conditional bistability (1) is a generic biophysical spike-dependent mechanism of layer V pyramidal neurons in the PFC and that (2) it accounts for essential neurodynamical features for the organization and flexibility of PFC persistent activity (the large irregularity and intertrial variability of the discharge and its organization under discrete stable states), which remain unexplained in a robust fashion by current models.

Brain stimulation modulates the excitability of neural circuits and drives neuroplasticity. While the local effects of stimulation have been an active area of investigation, the effects on large-scale networks remain largely unexplored. We studied stimulation-induced changes in network dynamics in two macaques. A large-scale optogenetic interface enabled simultaneous stimulation of excitatory neurons and electrocorticographic recording across primary somatosensory (S1) and motor (M1) cortex (Yazdan-Shahmorad et al., 2016). We tracked two measures of network connectivity, the network response to focal stimulation and the baseline coherence between pairs of electrodes; these were strongly correlated before stimulation. Within minutes, stimulation in S1 or M1 significantly strengthened the gross functional connectivity between these areas. At a finer scale, stimulation led to heterogeneous connectivity changes across the network. These changes reflected the correlations introduced by stimulation-evoked activity, consistent with Hebbian plasticity models. This work extends Hebbian plasticity models to large-scale circuits, with significant implications for stimulation-based neurorehabilitation.

The end of the critical period for primary visual cortex (V1) coincides with the deposition of perineuronal nets (PNN) onto Parvalbumin (PV) inhibitory neurons. Recently, we found that transplantation of embryonic inhibitory neurons into adult V1 reinstates a new critical period. Here we used Wisteria Floribunda Agglutinin (WFA) staining to compare the deposition of PNNs onto neurons during normal development and following transplantation at equivalent cell ages. In accord with previous findings, PV and PNN expression increases from negligible levels at postnatal day 14 (P14) to mature levels by P70. In contrast to P14, PNNs are found on transplanted PV neurons by 21 days after transplantation and persist to 105 days after transplantation. This precocious deposition was specific to PV neurons and excluded transplanted neurons expressing Somatostatin. Notably, the onset of PV expression in transplanted inhibitory neurons follows the timing of PV expression in juvenile V1. Moreover, transplantation has no discernible effect on host PNNs. The precocious deposition of PNNs onto transplanted PV neurons suggests that PNN expression identified by WFA does not reflect neuronal maturity and may be an inaccurate marker for transplant-induced plasticity of cortical circuits.

How structural connectivity (SC) gives rise to functional connectivity (FC) is not fully understood. Here we mathematically derive a simple relationship between SC measured from diffusion tensor imaging, and FC from resting state fMRI. We establish that SC and FC are related via (structural) Laplacian spectra, whereby FC and SC share eigenvectors and their eigenvalues are exponentially related. This gives, for the first time, a simple and analytical relationship between the graph spectra of structural and functional networks. Laplacian eigenvectors are shown to be good predictors of functional eigenvectors and networks based on independent component analysis of functional time series. A small number of Laplacian eigenmodes are shown to be sufficient to reconstruct FC matrices, serving as basis functions. This approach is fast, and requires no time-consuming simulations. It was tested on two empirical SC/FC datasets, and was found to significantly outperform generative model simulations of coupled neural masses.

BACKGROUND:Psychotic-like experiences (PLEs) are associated with lower social and occupational functioning, and lower executive function. Emerging evidence also suggests that PLEs reflect neural dysfunction resembling that of psychotic disorders. METHODS:The present study examined dynamic connectivity related to a measure of PLEs derived from the Achenbach Adult Self-Report, in an otherwise-healthy sample of adults from the Human Connectome Project. A total of 76 PLE-endorsing and 153 control participants were included in the final sample. To characterize network dysfunction, dynamic connectivity states were examined across large-scale resting-state networks using dynamic conditional correlation and k-means clustering. RESULTS:Three dynamic states were identified. The PLE-endorsing group spent more time than the control group in state 1, a state reflecting hyperconnectivity within visual regions and hypoconnectivity within the default mode network, and less time in state 2, a state characterized by robust within-network connectivity for all networks and strong default mode network anticorrelations. Within the PLE-endorsing group, worse executive function was associated with more time spent in and more transitions into state 1 and less time spent in and fewer transitions into state 3. CONCLUSIONS:PLEs are associated with altered large-scale brain dynamics, which tip the system away from spending more time in states reflecting more "typical" connectivity patterns toward more time in states reflecting visual hyperconnectivity and default mode hypoconnectivity.

Transgender individuals experience incongruence between their gender identity and birth-assigned sex. The resulting gender dysphoria (GD), which some gender-incongruent individuals experience, is theorized to be a consequence of atypical cerebral sexual differentiation, but support for this assertion is inconsistent. We recently found that GD is associated with disconnected networks involved in self-referential thinking and own body perception. Here, we investigate how these networks in trans men (assigned female at birth with male gender identity) are affected by testosterone. In 22 trans men, we obtained T1-weighted, diffusion-weighted, and resting-state functional magnetic resonance imaging scans before and after testosterone treatment, measuring cortical thickness (Cth), subcortical volumes, fractional anisotropy (FA), and functional connectivity. Nineteen cisgender controls (male and female) were also scanned twice. The medial prefrontal cortex (mPFC) was thicker in trans men than controls pretreatment, and remained unchanged posttreatment. Testosterone treatment resulted in increased Cth in the insular cortex, changes in cortico-cortical thickness covariation between mPFC and occipital cortex, increased FA in the fronto-occipital tract connecting these regions, and increased functional connectivity between mPFC and temporo-parietal junction, compared with controls. Concluding, in trans men testosterone treatment resulted in functional and structural changes in self-referential and own body perception areas.

Cellular interactions between delta and mu opioid receptors (DORs and MORs), including heteromerization, are thought to regulate opioid analgesia. However, the identity of the nociceptive neurons in which such interactions could occur in vivo remains elusive. Here we show that DOR-MOR co-expression is limited to small populations of excitatory interneurons and projection neurons in the spinal cord dorsal horn and unexpectedly predominates in ventral horn motor circuits. Similarly, DOR-MOR co-expression is rare in parabrachial, amygdalar, and cortical brain regions processing nociceptive information. We further demonstrate that in the discrete DOR-MOR co-expressing nociceptive neurons, the two receptors internalize and function independently. Finally, conditional knockout experiments revealed that DORs selectively regulate mechanical pain by controlling the excitability of somatostatin-positive dorsal horn interneurons. Collectively, our results illuminate the functional organization of DORs and MORs in CNS pain circuits and reappraise the importance of DOR-MOR cellular interactions for developing novel opioid analgesics.

BackgroundHalf of all new alcohol initiates are between 12 and 17 years old. This is a period of intense neurodevelopment, including changes in functional connectivity patterns among higher-order function areas. It is crucial to understand how alcohol-related neurotoxicity may be influenced by drinking onset age.DesignThis study prospectively examined the effects of age of first drink on frontoparietal context-dependent functional connectivity (cdFC) during a visual working memory task. Youth 13.5 years of age (SD = 1.2) underwent a neuropsychological and neuroimaging session before drinking initiation and at follow-up 6 years later. Hierarchical linear regressions examined if youth with earlier ages of onset for first and weekly alcohol use showed higher follow-up cdFC between the dorsolateral prefrontal cortex and posterior parietal cortex regions of interest and whole-brain exploratory regions, controlling for pre-drinking cdFC. Higher follow-up cdFC was hypothesized to be correlated with poorer performances in neuropsychological performance.ResultsExploratory whole-brain analyses showed that, as hypothesized, earlier ages of weekly drinking onset were associated with higher cdFC between the bilateral posterior cingulate and cortical and subcortical areas implicated in attentional processes, which was in turn associated with poorer performance on neuropsychological tasks of attention, ps

Biological oscillations can be controlled by a small population of rhythmic pacemaker cells, or in the brain, they also can emerge from complex cellular and circuit-level interactions. Whether and how these mechanisms are combined to give rise to oscillatory patterns that govern cognitive function are not well understood. For example, the activity of hippocampal networks is temporally coordinated by a 7- to 9-Hz local field potential (LFP) theta rhythm, yet many individual cells decouple from the LFP frequency to oscillate at frequencies ∼1 Hz higher. To better understand the network interactions that produce these complex oscillatory patterns, we asked whether the relative frequency difference between LFP and individual cells is retained when the LFP frequency is perturbed experimentally. We found that rhythmic optogenetic stimulation of medial septal GABAergic neurons controlled the hippocampal LFP frequency outside of the endogenous theta range, even during behavioral states when endogenous mechanisms would otherwise have generated 7- to 9-Hz theta oscillations. While the LFP frequency matched the optogenetically induced stimulation frequency, the oscillation frequency of individual hippocampal cells remained broadly distributed, and in a subset of cells including interneurons, it was accelerated beyond the new base LFP frequency. The inputs from septal GABAergic neurons to the hippocampus, therefore, do not appear to directly control the cellular oscillation frequency but rather engage cellular and circuit mechanisms that accelerate the rhythmicity of individual cells. Thus, theta oscillations are an example of cortical oscillations that combine inputs from a subcortical pacemaker with local computations to generate complex oscillatory patterns that support cognitive functions.

Cortical reorganization after stroke is thought to underlie functional improvement. Patterns of reorganization may differ depending on the amount of time since the stroke or the degree of improvement. We investigated these issues in a study of brain connectivity changes with aphasia therapy. Twelve individuals with chronic aphasia participated in a 6-week trial of imitation-based speech therapy. We assessed improvement on a repetition test and analyzed effective connectivity during functional magnetic resonance imaging of a speech observation task before and after therapy. Using structural equation modeling, patient networks were compared with a model derived from healthy controls performing the same task. Independent of the amount of time since the stroke, patients demonstrating behavioral improvement had networks that reorganized to be more similar to controls in two functional pathways in the left hemisphere. Independent of behavioral improvement, patients with remote infarcts (2-7 years poststroke; n = 5) also reorganized to more closely resemble controls in one of these pathways. Patients with far removed injury (>10 years poststroke; n = 3) did not show behavioral improvement and, despite similarities to the normative model and overall network heterogeneity, reorganized to be less similar to controls following therapy in a distinct right-lateralized pathway. Behavioral improvement following aphasia therapy was associated with connectivity more closely approximating that of healthy controls. Individuals who had a stroke more than a decade before testing also showed plasticity, with a few pathways becoming less like controls, possibly representing compensation. Better understanding of these mechanisms may help direct targeted brain stimulation.

We live in a world that is largely socially constructed, and we are constantly involved in and fundamentally influenced by a broad array of complex social interactions. Social behaviors among conspecifics, either conflictive or cooperative, are exhibited by all sexually reproducing animal species and are essential for the health, survival, and reproduction of animals. Conversely, impairment in social function is a prominent feature of several neuropsychiatric disorders, such as autism spectrum disorders and schizophrenia. Despite the importance of social behaviors, many fundamental questions remain unanswered. How is social sensory information processed and integrated in the nervous system? How are different social behavioral decisions selected and modulated in brain circuits? Here we discuss conceptual issues and recent advances in our understanding of brain regions and neural circuit mechanisms underlying the regulation of social behaviors.

Patients with Alzheimer's disease vary in their ability to sustain cognitive abilities in the presence of brain pathology. A major open question is which brain mechanisms may support higher reserve capacity, i.e. relatively high cognitive performance at a given level of Alzheimer's pathology. Higher functional MRI-assessed functional connectivity of a hub in the left frontal cortex is a core candidate brain mechanism underlying reserve as it is associated with education (i.e. a protective factor often associated with higher reserve) and attenuated cognitive impairment in prodromal Alzheimer's disease. However, no study has yet assessed whether such hub connectivity of the left frontal cortex supports reserve throughout the evolution of pathological brain changes in Alzheimer's disease, including the presymptomatic stage when cognitive decline is subtle. To address this research gap, we obtained cross-sectional resting state functional MRI in 74 participants with autosomal dominant Alzheimer's disease, 55 controls from the Dominantly Inherited Alzheimer's Network and 75 amyloid-positive elderly participants, as well as 41 amyloid-negative cognitively normal elderly subjects from the German Center of Neurodegenerative Diseases multicentre study on biomarkers in sporadic Alzheimer's disease. For each participant, global left frontal cortex connectivity was computed as the average resting state functional connectivity between the left frontal cortex (seed) and each voxel in the grey matter. As a marker of disease stage, we applied estimated years from symptom onset in autosomal dominantly inherited Alzheimer's disease and cerebrospinal fluid tau levels in sporadic Alzheimer's disease cases. In both autosomal dominant and sporadic Alzheimer's disease patients, higher levels of left frontal cortex connectivity were correlated with greater education. For autosomal dominant Alzheimer's disease, a significant left frontal cortex connectivity × estimated years of onset interaction was found, indicating slower decline of memory and global cognition at higher levels of connectivity. Similarly, in sporadic amyloid-positive elderly subjects, the effect of tau on cognition was attenuated at higher levels of left frontal cortex connectivity. Polynomial regression analysis showed that the trajectory of cognitive decline was shifted towards a later stage of Alzheimer's disease in patients with higher levels of left frontal cortex connectivity. Together, our findings suggest that higher resilience against the development of cognitive impairment throughout the early stages of Alzheimer's disease is at least partially attributable to higher left frontal cortex-hub connectivity.

The default mode network (DMN) supports memory functioning and may be sensitive to preclinical Alzheimer's pathology. Little is known, however, about the longitudinal trajectory of this network's intrinsic functional connectivity (FC). In this study, we evaluated longitudinal FC in 111 cognitively normal older human adults (ages 49-87, 46 women/65 men), 92 of whom had at least three task-free fMRI scans (n = 353 total scans). Whole-brain FC and three DMN subnetworks were assessed: (1) within-DMN, (2) between anterior and posterior DMN, and (3) between medial temporal lobe network and posterior DMN. Linear mixed-effects models demonstrated significant baseline age × time interactions, indicating a nonlinear trajectory. There was a trend toward increasing FC between ages 50-66 and significantly accelerating declines after age 74. A similar interaction was observed for whole-brain FC. APOE status did not predict baseline connectivity or change in connectivity. After adjusting for network volume, changes in within-DMN connectivity were specifically associated with changes in episodic memory and processing speed but not working memory or executive functions. The relationship with processing speed was attenuated after covarying for white matter hyperintensities (WMH) and whole-brain FC, whereas within-DMN connectivity remained associated with memory above and beyond WMH and whole-brain FC. Whole-brain and DMN FC exhibit a nonlinear trajectory, with more rapid declines in older age and possibly increases in connectivity early in the aging process. Within-DMN connectivity is a marker of episodic memory performance even among cognitively healthy older adults.SIGNIFICANCE STATEMENT Default mode network and whole-brain connectivity, measured using task-free fMRI, changed nonlinearly as a function of age, with some suggestion of early increases in connectivity. For the first time, longitudinal changes in DMN connectivity were shown to correlate with changes in episodic memory, whereas volume changes in relevant brain regions did not. This relationship was not accounted for by white matter hyperintensities or mean whole-brain connectivity. Functional connectivity may be an early biomarker of changes in aging but should be used with caution given its nonmonotonic nature, which could complicate interpretation. Future studies investigating longitudinal network changes should consider whole-brain changes in connectivity.

Behavioral tasks involving auditory cues activate inhibitory neurons within auditory cortex, leading to a reduction in the amplitude of auditory evoked response potentials (ERPs). One hypothesis is that this process, termed "task engagement," may enable context-dependent behaviors. Here we set out to determine (1) whether the medial prefrontal cortex (mPFC) plays a role in task engagement and (2) how task engagement relates to the context-dependent processing of auditory cues in male and female mice performing a decision-making task that can be guided by either auditory or visual cues. We found that, in addition to auditory ERP suppression, task engagement is associated with increased mPFC activity and an increase in theta band (4-7 Hz) synchronization between the mPFC and auditory cortex. Optogenetically inhibiting the mPFC eliminates the task engagement-induced auditory ERP suppression, while also preventing mice from switching between auditory and visual cue-based rules. However, mPFC inhibition, which eliminates task engagement-induced auditory ERP suppression, did not prevent mice from making decisions based on auditory cues. Furthermore, a more specific manipulation, selective disruption of mPFC outputs to the mediodorsal (MD) thalamus, is sufficient to prevent switching between auditory and visual rules but does not affect auditory ERPs. Based on these findings, we conclude that (1) the mPFC contributes to both task engagement and behavioral flexibility; (2) mPFC-MD projections are important for behavioral flexibility but not task engagement; and (3) task engagement, evidenced by the suppression of cortical responses to sensory input, is not required for sensory cue-guided decision making.SIGNIFICANCE STATEMENT When rodents perform choice-selection tasks based on sensory cues, neural responses to these cues are modulated compared with task-free conditions. Here we demonstrate that this phenomenon depends on the prefrontal cortex and thus represents a form of "top-down" regulation. However, we also show that this phenomenon is not critical for task performance, as rodents can make decisions based on specific sensory cues even when the task-dependent modulation of responses to those cues is abolished. Furthermore, disrupting one specific set of prefrontal outputs impairs rule switching but not the task-dependent modulation of sensory responses. These results show that the prefrontal cortex comprises multiple circuits that mediate dissociable functions related to behavioral flexibility and sensory processing.

Sleep suffers during adolescence and is related to academic, emotional and social behaviors. How this normative change relates to ongoing brain development remains unresolved. The default mode network (DMN), a large-scale brain network important for complex cognition and socioemotional processing, undergoes intra-network integration and inter-network segregation during adolescence. Using resting state functional connectivity and actigraphy over 14 days, we examined correlates of naturalistic individual differences in sleep duration and quality in the DMN at rest in 45 human adolescents (ages 14-18). Variation in sleep quality, but not duration, was related to weaker intrinsic DMN connectivity, such that those with worse quality sleep evinced weaker intra-network connectivity at rest. These novel findings suggest sleep quality, a relatively unexplored sleep index, is related to adolescent brain function in a network that contributes to behavioral maturation and undergoes development during adolescence.

Understanding another's actions, including what they are doing and why they are doing it, can be difficult for individuals with autism spectrum disorder (ASD). This understanding is supported by the action observation (AON) and mentalizing (MZN) networks, as well as the superior temporal sulcus. We examined these areas in children with ASD and typically developing controls by having participants view eating and placing actions performed in conventional and unconventional ways while functional magnetic resonance images were collected. We found an effect of action-type, but not conventionality, in both groups, and a between groups difference only when viewing conventional eating actions. Findings suggest there are not global AON/MZN deficits in ASD, and observing unconventional actions may not spontaneously activate the MZN.

Cognitive function varies substantially and serves as a key predictor of outcome and response to intervention in autism spectrum disorder (ASD), yet we know little about the neurobiological mechanisms that underlie cognitive function in children with ASD. The dynamics of neuronal oscillations in the alpha range (6-12 Hz) are associated with cognition in typical development. Peak alpha frequency is also highly sensitive to developmental changes in neural networks, which underlie cognitive function, and therefore, it holds promise as a developmentally sensitive neural marker of cognitive function in ASD. Here, we measured peak alpha band frequency under a task-free condition in a heterogeneous sample of children with ASD (N = 59) and age-matched typically developing (TD) children (N = 38). At a group level, peak alpha frequency was decreased in ASD compared to TD children. Moreover, within the ASD group, peak alpha frequency correlated strongly with non-verbal cognition. As peak alpha frequency reflects the integrity of neural networks, our results suggest that deviations in network development may underlie cognitive function in individuals with ASD. By shedding light on the neurobiological correlates of cognitive function in ASD, our findings lay the groundwork for considering peak alpha frequency as a useful biomarker of cognitive function within this population which, in turn, will facilitate investigations of early markers of cognitive impairment and predictors of outcome in high risk infants.

The 2016 U.S. presidential election yielded distress among many individuals who identify with historically marginalized groups. We used functional magnetic resonance imaging and psychological measures to test the hypotheses that neural response to reward, probing the nucleus accumbens (NAcc) and medial prefrontal cortex (mPFC), and social support would ameliorate the effects of election distress among those who felt negatively affected by the result. Within 4 months of the 2016 U.S. presidential election, we tested human participants who felt affected by the election result (n = 40, Mage = 21.9 years, 28 female) and control participants (n = 20, Mage = 20.25 years, 12 female) who did not feel affected by the election result. Election-related distress significantly differed between the groups, and distress accounted for over half of the relationship between discrimination experiences and depression symptoms among affected individuals. NAcc activation, connectivity between the NAcc and mPFC, and family support moderated the associations between election distress and depression symptoms. Prior work has primarily investigated mesolimbic circuitry in reward and motivation contexts, but our findings extend the relevance of functioning in this circuitry to ameliorating psychological manifestations of acute distress after shifts in political climate. These findings highlight the psychological effects of this important historic event and identify neurobiological and social mechanisms associated with individual differences in response to election distress.SIGNIFICANCE STATEMENT The 2016 U.S. presidential election was psychologically distressing for many individuals. In this study, election-related distress was linked to depression symptomology for affected individuals but not control individuals. However, among individuals distressed by the election, those with greater neural response to reward and higher family support were protected against these depressive symptoms. Previous research has examined how neural response to reward after a discrete event ameliorates clinical symptoms. The current study extends this knowledge by demonstrating that both the brain and social support may play influential roles in dampening affective responses to ongoing and anticipated distress related to political climate. Leveraging this finding to enact interventions that dampen continuous distress, political or otherwise, is a promising endeavor for future research.

Anorexia nervosa (AN) is a serious eating disorder characterized by self-starvation and extreme weight loss. Pseudoatrophic brain changes are often readily visible in individual brain scans, and AN may be a valuable model disorder to study structural neuroplasticity. Structural magnetic resonance imaging studies have found reduced gray matter volume and cortical thinning in acutely underweight patients to normalize following successful treatment. However, some well-controlled studies have found regionally greater gray matter and persistence of structural alterations following long-term recovery. Findings from diffusion tensor imaging studies of white matter integrity and connectivity are also inconsistent. Furthermore, despite the severity of AN, the number of existing structural neuroimaging studies is still relatively low, and our knowledge of the underlying cellular and molecular mechanisms for macrostructural brain changes is rudimentary. We critically review the current state of structural neuroimaging in AN and discuss the potential neurobiological basis of structural brain alterations in the disorder, highlighting impediments to progress, recent developments, and promising future directions. In particular, we argue for the utility of more standardized data collection, adopting a connectomics approach to understanding brain network architecture, employing advanced magnetic resonance imaging methods that quantify biomarkers of brain tissue microstructure, integrating data from multiple imaging modalities, strategic longitudinal observation during weight restoration, and large-scale data pooling. Our overarching objective is to motivate carefully controlled research of brain structure in eating disorders, which will ultimately help predict therapeutic response and improve treatment.

Complex spatial working memory tasks have been shown to require both hippocampal sharp-wave ripple (SWR) activity and dentate gyrus (DG) neuronal activity. We therefore asked whether DG inputs to CA3 contribute to spatial working memory by promoting SWR generation. Recordings from DG and CA3 while rats performed a dentate-dependent working memory task on an eight-arm radial maze revealed that the activity of dentate neurons and the incidence rate of SWRs both increased during reward consumption. We then found reduced reward-related CA3 SWR generation without direct input from dentate granule neurons. Furthermore, CA3 cells with place fields in not-yet-visited arms preferentially fired during SWRs at reward locations, and these prospective CA3 firing patterns were more pronounced for correct trials and were dentate-dependent. These results indicate that coordination of CA3 neuronal activity patterns by DG is necessary for the generation of neuronal firing patterns that support goal-directed behavior and memory.

Fast-spiking interneurons (FSIs) are a prominent class of forebrain GABAergic cells implicated in two seemingly independent network functions: gain control and network plasticity. Little is known, however, about how these roles interact. Here, we use a combination of cell-type-specific ablation, optogenetics, electrophysiology, imaging, and behavior to describe a unified mechanism by which striatal FSIs control burst firing, calcium influx, and synaptic plasticity in neighboring medium spiny projection neurons (MSNs). In vivo silencing of FSIs increased bursting, calcium transients, and AMPA/NMDA ratios in MSNs. In a motor sequence task, FSI silencing increased the frequency of calcium transients but reduced the specificity with which transients aligned to individual task events. Consistent with this, ablation of FSIs disrupted the acquisition of striatum-dependent egocentric learning strategies. Together, our data support a model in which feedforward inhibition from FSIs temporally restricts MSN bursting and calcium-dependent synaptic plasticity to facilitate striatum-dependent sequence learning.

OBJECTIVE Dysfunction of distributed neural networks underlies many brain disorders. The development of neuromodulation therapies depends on a better understanding of these networks. Invasive human brain recordings have a favorable temporal and spatial resolution for the analysis of network phenomena but have generally been limited to acute intraoperative recording or short-term recording through temporarily externalized leads. Here, the authors describe their initial experience with an investigational, first-generation, totally implantable, bidirectional neural interface that allows both continuous therapeutic stimulation and recording of field potentials at multiple sites in a neural network. METHODS Under a physician-sponsored US Food and Drug Administration investigational device exemption, 5 patients with Parkinson's disease were implanted with the Activa PC+S system (Medtronic Inc.). The device was attached to a quadripolar lead placed in the subdural space over motor cortex, for electrocorticography potential recordings, and to a quadripolar lead in the subthalamic nucleus (STN), for both therapeutic stimulation and recording of local field potentials. Recordings from the brain of each patient were performed at multiple time points over a 1-year period. RESULTS There were no serious surgical complications or interruptions in deep brain stimulation therapy. Signals in both the cortex and the STN were relatively stable over time, despite a gradual increase in electrode impedance. Canonical movement-related changes in specific frequency bands in the motor cortex were identified in most but not all recordings. CONCLUSIONS The acquisition of chronic multisite field potentials in humans is feasible. The device performance characteristics described here may inform the design of the next generation of totally implantable neural interfaces. This research tool provides a platform for translating discoveries in brain network dynamics to improved neurostimulation paradigms. Clinical trial registration no.: NCT01934296 (clinicaltrials.gov).

Understanding the relationship between the dynamics of neural processes and the anatomical substrate of the brain is a central question in neuroscience. On the one hand, modern neuroimaging technologies, such as diffusion tensor imaging, can be used to construct structural graphs representing the architecture of white matter streamlines linking cortical and subcortical structures. On the other hand, temporal patterns of neural activity can be used to construct functional graphs representing temporal correlations between brain regions. Although some studies provide evidence that whole-brain functional connectivity is shaped by the underlying anatomy, the observed relationship between function and structure is weak, and the rules by which anatomy constrains brain dynamics remain elusive. In this article, we introduce a methodology to map the functional connectivity of a subject at rest from his or her structural graph. Using our methodology, we are able to systematically account for the role of structural walks in the formation of functional correlations. Furthermore, in our empirical evaluations, we observe that the eigenmodes of the mapped functional connectivity are associated with activity patterns associated with different cognitive systems.

Addiction involves a predominance of habitual control mediated through action selection processes in dorsal striatum. Research has largely focused on neural mechanisms mediating a proposed progression from ventral to dorsal lateral striatal control in addiction. However, over reliance on habit striatal processes may also arise from reduced cortical input to striatum, thereby disrupting executive control over action selection. Here, we identify novel mechanisms through which chronic intermittent ethanol exposure and withdrawal (CIE) disrupts top-down control over goal-directed action selection processes to produce habits. We find CIE results in decreased excitability of orbital frontal cortex (OFC) excitatory circuits supporting goal-directed control, and, strikingly, selectively reduces OFC output to the direct output pathway in dorsal medial striatum. Increasing the activity of OFC circuits restores goal-directed control in CIE-exposed mice. Our findings show habitual control in alcohol dependence can arise through disrupted communication between top-down, goal-directed processes onto basal ganglia pathways controlling action selection.

Estimation of functional connectivity (FC) has become an increasingly powerful tool for investigating healthy and abnormal brain function. Static connectivity, in particular, has played a large part in guiding conclusions from the majority of resting-state functional MRI studies. However, accumulating evidence points to the presence of temporal fluctuations in FC, leading to increasing interest in estimating FC as a dynamic quantity. One central issue that has arisen in this new view of connectivity is the dramatic increase in complexity caused by dynamic functional connectivity (dFC) estimation. To computationally handle this increased complexity, a limited set of dFC properties, primarily the mean and variance, have generally been considered. Additionally, it remains unclear how to integrate the increased information from dFC into pattern recognition techniques for subject-level prediction. In this study, we propose an approach to address these two issues based on a large number of previously unexplored temporal and spectral features of dynamic functional connectivity. A Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to estimate time-varying patterns of functional connectivity between resting-state networks. Time-frequency analysis is then performed on dFC estimates, and a large number of previously unexplored temporal and spectral features drawn from signal processing literature are extracted for dFC estimates. We apply the investigated features to two neurologic populations of interest, healthy controls and patients with temporal lobe epilepsy, and show that the proposed approach leads to substantial increases in predictive performance compared to both traditional estimates of static connectivity as well as current approaches to dFC. Variable importance is assessed and shows that there are several quantities that can be extracted from dFC signal which are more informative than the traditional mean or variance of dFC. This work illuminates many previously unexplored facets of the dynamic properties of functional connectivity between resting-state networks, and provides a platform for dynamic functional connectivity analysis that facilitates its usage as an investigative measure for healthy as well as abnormal brain function.

OBJECTIVE:The ability to flexibly switch between tasks is considered an important component of cognitive control that involves frontal and parietal cortical areas. The present study was designed to characterize network dynamics across multiple brain regions during task switching. METHOD:Functional magnetic resonance images (fMRI) were captured during a standard rule-switching task to identify switching-related brain regions. Multiregional psychophysiological interaction (PPI) analysis was used to examine effective connectivity between these regions. RESULTS:During switching trials, behavioral performance declined and activation of a generic cognitive control network increased. Concurrently, task-related connectivity increased within and between cingulo-opercular and fronto-parietal cognitive control networks. Notably, the left inferior frontal junction (IFJ) was most consistently coactivated with the 2 cognitive control networks. Furthermore, switching-dependent effective connectivity was negatively correlated with behavioral switch costs. The strength of effective connectivity between left IFJ and other regions in the networks predicted individual differences in switch costs. CONCLUSIONS:Task switching was supported by coactivated connections within cognitive control networks, with left IFJ potentially acting as a key hub between the fronto-parietal and cingulo-opercular networks. (PsycINFO Database Record

Transcranial direct current stimulation (tDCS) is an innovative technique recently shown to improve language outcomes even in neurodegenerative conditions such as primary progressive aphasia (PPA), but the underlying brain mechanisms are not known. The present study tested whether the additional language gains with repetitive tDCS (over sham) in PPA are caused by changes in functional connectivity between the stimulated area (the left inferior frontal gyrus (IFG)) and the rest of the language network. We scanned 24 PPA participants (11 female) before and after language intervention (written naming/spelling) with a resting-state fMRI sequence and compared changes before and after three weeks of tDCS or sham coupled with language therapy. We correlated changes in the language network as well as in the default mode network (DMN) with language therapy outcome measures (letter accuracy in written naming). Significant tDCS effects in functional connectivity were observed between the stimulated area and other language network areas and between the language network and the DMN. TDCS over the left IFG lowered the connectivity between the above pairs. Changes in functional connectivity correlated with improvement in language scores (letter accuracy as a proxy for written naming) evaluated before and after therapy. These results suggest that one mechanism for anodal tDCS over the left IFG in PPA is a decrease in functional connectivity (compared to sham) between the stimulated site and other posterior areas of the language network. These results are in line with similar decreases in connectivity observed after tDCS over the left IFG in aging and other neurodegenerative conditions.

Several neuroimaging studies have shown that listening to music activates brain regions that reside in the motor system, even when there is no overt movement. However, many of these studies report the activation of varying motor system areas that include the primary motor cortex, supplementary motor area, dorsal and ventral pre-motor areas and parietal regions. In order to examine what specific roles are played by various motor regions during music perception, we used activation likelihood estimation (ALE) to conduct a meta-analysis of neuroimaging literature on passive music listening. After extensive search of the literature, 42 studies were analyzed resulting in a total of 386 unique subjects contributing 694 activation foci in total. As suspected, auditory activations were found in the bilateral superior temporal gyrus, transverse temporal gyrus, insula, pyramis, bilateral precentral gyrus, and bilateral medial frontal gyrus. We also saw the widespread activation of motor networks including left and right lateral premotor cortex, right primary motor cortex, and the left cerebellum. These results suggest a central role of the motor system in music and rhythm perception. We discuss these findings in the context of the Action Simulation for Auditory Prediction (ASAP) model and other predictive coding accounts of brain function.

Brain anatomy and physiology support the human ability to navigate a complex space of perceptions and actions. To maneuver across an ever-changing landscape of mental states, the brain invokes cognitive control-a set of dynamic processes that engage and disengage different groups of brain regions to modulate attention, switch between tasks, and inhibit prepotent responses. Current theory posits that correlated and anticorrelated brain activity may signify cooperative and competitive interactions between brain areas that subserve adaptive behavior. In this study, we use a quantitative approach to identify distinct topological motifs of functional interactions and examine how their expression relates to cognitive control processes and behavior. In particular, we acquire fMRI BOLD signal in twenty-eight healthy subjects as they perform two cognitive control tasks-a Stroop interference task and a local-global perception switching task using Navon figures-each with low and high cognitive control demand conditions. Based on these data, we construct dynamic functional brain networks and use a parts-based, network decomposition technique called non-negative matrix factorization to identify putative cognitive control subgraphs whose temporal expression captures distributed network structures involved in different phases of cooperative and competitive control processes. Our results demonstrate that temporal expression of the subgraphs fluctuate alongside changes in cognitive demand and are associated with individual differences in task performance. These findings offer insight into how coordinated changes in the cooperative and competitive roles of cognitive systems map trajectories between cognitively demanding brain states.

Poor executive functioning increases risk of decline in Mild Cognitive Impairment (MCI). Executive functioning can be conceptualized within the framework of working memory. While some components are responsible for maintaining representations in working memory, the central executive is involved in the manipulation of information and creation of new representations. We aimed to examine the neural correlates of these components of working memory using a maintenance working memory and visuospatial reasoning task. Twenty-five patients with amnestic MCI and 19 elderly controls (EC) completed functional MRI during reasoning and maintenance working memory tasks. In MCI, maintenance working memory was associated with hypoactivation of right frontoparietal regions and hyperactivation of left prefrontal cortex, coupled with attenuation of default mode network (DMN) relative to EC. During reasoning, MCI showed hypoactivation of parietal regions, coupled with attenuation of anterior DMN and increased deactivation of posterior DMN relative to EC. Comparing the reasoning task to the maintenance working memory task yields the central executive. In MCI, the central executive showed hypoactivation of right parietal lobe and increased deactivation of posterior DMN compared to EC. Consistent with prior work on executive functioning, MCI show different neural circuitry during visuospatial reasoning, including changes to both task positive frontoparietal regions, as well as to deactivation patterns within the DMN. Both hyperactivation of task positive networks and increased deactivation of DMN may be compensatory.

The hippocampus is well known as a central site for memory processing-critical for storing and later retrieving the experiences events of daily life so they can be used to shape future behavior. Much of what we know about the physiology underlying hippocampal function comes from spatial navigation studies in rodents, which have allowed great strides in understanding how the hippocampus represents experience at the cellular level. However, it remains a challenge to reconcile our knowledge of spatial encoding in the hippocampus with its demonstrated role in memory-dependent tasks in both humans and other animals. Moreover, our understanding of how networks of neurons coordinate their activity within and across hippocampal subregions to enable the encoding, consolidation, and retrieval of memories is incomplete. In this chapter, we explore how information may be represented at the cellular level and processed via coordinated patterns of activity throughout the subregions of the hippocampal network.

Patients with Generalized Anxiety Disorder (GAD) and Major Depressive Disorder (MDD) show between-group comorbidity and symptom overlap, and within-group heterogeneity. Resting state functional connectivity might provide an alternate, biologically informed means by which to stratify patients with GAD or MDD. Resting state functional magnetic resonance imaging data were acquired from 23 adults with GAD, 21 adults with MDD, and 27 healthy adult control participants. We investigated whether within- or between-network connectivity indices from five resting state networks predicted scores on continuous measures of depression and anxiety. Successful predictors were used to stratify participants into two new groups. We examined whether this stratification predicted attentional bias towards threat and whether this varied between patients and controls. Depression scores were linked to elevated connectivity within a limbic network including the amygdala, hippocampus, VMPFC and subgenual ACC. Patients with GAD or MDD with high limbic connectivity showed poorer performance on an attention-to-threat task than patients with low limbic connectivity. No parallel effect was observed for control participants, resulting in an interaction of clinical status by resting state group. Our findings provide initial evidence for the external validity of stratification of MDD and GAD patients by functional connectivity markers. This stratification cuts across diagnostic boundaries and might valuably inform future intervention studies. Our findings also highlight that biomarkers of interest can have different cognitive correlates in individuals with versus without clinically significant symptomatology. This might reflect protective influences leading to resilience in some individuals but not others.

Reduced fractional anisotropy (FA) is a well-established correlate of schizophrenia, but it remains unclear whether these tensor-based differences are the result of axon damage and/or organizational changes and whether the changes are progressive in the adult course of illness. Diffusion MRI data were collected in 81 schizophrenia patients (54 first episode and 27 chronic) and 64 controls. Analysis of FA was combined with "fixel-based" analysis, the latter of which leverages connectivity and crossing-fiber information to assess both fiber bundle density and organizational complexity (i.e., presence and magnitude of off-axis diffusion signal). Compared with controls, patients with schizophrenia displayed clusters of significantly lower FA in the bilateral frontal lobes, right dorsal centrum semiovale, and the left anterior limb of the internal capsule. All FA-based group differences overlapped substantially with regions containing complex fiber architecture. FA within these clusters was positively correlated with principal axis fiber density, but inversely correlated with both secondary/tertiary axis fiber density and voxel-wise fiber complexity. Crossing fiber complexity had the strongest (inverse) association with FA (r = -0.82). When crossing fiber structure was modeled in the MRtrix fixel-based analysis pipeline, patients exhibited significantly lower fiber density compared to controls in the dorsal and posterior corpus callosum (central, postcentral, and forceps major). Findings of lower FA in patients with schizophrenia likely reflect two inversely related signals: reduced density of principal axis fiber tracts and increased off-axis diffusion sources. Whereas the former confirms at least some regions where myelin and or/axon count are lower in schizophrenia, the latter indicates that the FA signal from principal axis fiber coherence is broadly contaminated by macrostructural complexity, and therefore does not necessarily reflect microstructural group differences. These results underline the need to move beyond tensor-based models in favor of acquisition and analysis techniques that can help disambiguate different sources of white matter disruptions associated with schizophrenia.

BACKGROUND:Anhedonia, the diminished ability to experience pleasure, is an important dimensional entity linked to depression, schizophrenia, and other emotional disorders, but its origins and mechanisms are poorly understood. We have previously identified anhedonia, manifest as decreased sucrose preference and social play, in adolescent male rats that experienced chronic early-life adversity/stress (CES). Here we probed the molecular, cellular, and circuit processes underlying CES-induced anhedonia and tested them mechanistically. METHODS:We examined functional brain circuits and neuronal populations activated by social play in adolescent CES and control rats. Structural connectivity between stress- and reward-related networks was probed using high-resolution diffusion tensor imaging, and cellular/regional activation was probed using c-Fos. We employed viral-genetic approaches to reduce corticotropin-releasing hormone (Crh) expression in the central nucleus of the amygdala in anhedonic rats, and tested for anhedonia reversal in the same animals. RESULTS:Sucrose preference was reduced in adolescent CES rats. Social play, generally considered an independent measure of pleasure, activated brain regions involved in reward circuitry in both control and CES groups. In CES rats, social play activated Crh-expressing neurons in the central nucleus of the amygdala, typically involved in anxiety/fear, indicating aberrant functional connectivity of pleasure/reward and fear circuits. Diffusion tensor imaging tractography revealed increased structural connectivity of the amygdala to the medial prefrontal cortex in CES rats. Crh-short hairpin RNA, but not control short hairpin RNA, given into the central nucleus of the amygdala reversed CES-induced anhedonia without influencing other emotional measures. CONCLUSIONS:These findings robustly demonstrate aberrant interactions of stress and reward networks after early-life adversity and suggest mechanistic roles for Crh-expressing amygdala neurons in emotional deficits portending major neuropsychiatric disorders.