Functional neuroimaging as a catalyst for integrated neuroscience.

Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.

The impact of the human thalamus on brain-wide information processing.

The thalamus is a small, bilateral structure in the diencephalon that integrates signals from many areas of the CNS. This critical anatomical position allows the thalamus to influence whole-brain activity and adaptive behaviour. However, traditional research paradigms have struggled to attribute specific functions to the thalamus, and it has remained understudied in the human neuroimaging literature. Recent advances in analytical techniques and increased accessibility to large, high-quality data sets have brought forth a series of studies and findings that (re-)establish the thalamus as a core region of interest in human cognitive neuroscience, a field that otherwise remains cortico-centric. In this Perspective, we argue that using whole-brain neuroimaging approaches to investigate the thalamus and its interaction with the rest of the brain is key for understanding systems-level control of information processing. To this end, we highlight the role of the thalamus in shaping a range of functional signatures, including evoked activity, interregional connectivity, network topology and neuronal variability, both at rest and during the performance of cognitive tasks.

A thalamocortical substrate for integrated information via critical synchronous bursting.

Understanding the neurobiological mechanisms underlying consciousness remains a significant challenge. Recent evidence suggests that the coupling between distal-apical and basal-somatic dendrites in thick-tufted layer 5 pyramidal neurons (L5PN), regulated by the nonspecific-projecting thalamus, is crucial for consciousness. Yet, it is uncertain whether this thalamocortical mechanism can support emergent signatures of consciousness, such as integrated information. To address this question, we constructed a biophysical network of dual-compartment thick-tufted L5PN, with dendrosomatic coupling controlled by thalamic inputs. Our findings demonstrate that integrated information is maximized when nonspecific thalamic inputs drive the system into a regime of time-varying synchronous bursting. Here, the system exhibits variable spiking dynamics with broad pairwise correlations, supporting the enhanced integrated information. Further, the observed peak in integrated information aligns with criticality signatures and empirically observed layer 5 pyramidal bursting rates. These results suggest that the thalamocortical core of the mammalian brain may be evolutionarily configured to optimize effective information processing, providing a potential neuronal mechanism that integrates microscale theories with macroscale signatures of consciousness.

Abnormal higher-order network interactions in Parkinson's disease visual hallucinations.

Visual hallucinations in Parkinson's disease can be viewed from a systems-level perspective, whereby dysfunctional communication between brain networks responsible for perception predisposes a person to hallucinate. To this end, abnormal functional interactions between higher-order and primary sensory networks have been implicated in the pathophysiology of visual hallucinations in Parkinson's disease, however the precise signatures remain to be determined. Dimensionality reduction techniques offer a novel means for simplifying the interpretation of multidimensional brain imaging data, identifying hierarchical patterns in the data that are driven by both within- and between-functional network changes. Here, we applied two complementary non-linear dimensionality reduction techniques-diffusion-map embedding and t-distributed stochastic neighbour embedding (t-SNE)-to resting state functional MRI data, in order to characterize the altered functional hierarchy associated with susceptibility to visual hallucinations. Our study involved 77 people with Parkinson's disease (31 with hallucinations; 46 without hallucinations) and 19 age-matched healthy control subjects. In patients with visual hallucinations, we found compression of the unimodal-heteromodal gradient consistent with increased functional integration between sensory and higher order networks. This was mirrored in a traditional functional connectivity analysis, which showed increased connectivity between the visual and default mode networks in the hallucinating group. Together, these results suggest a route by which higher-order regions may have excessive influence over earlier sensory processes, as proposed by theoretical models of hallucinations across disorders. By contrast, the t-SNE analysis identified distinct alterations in prefrontal regions, suggesting an additional layer of complexity in the functional brain network abnormalities implicated in hallucinations, which was not apparent in traditional functional connectivity analyses. Together, the results confirm abnormal brain organization associated with the hallucinating phenotype in Parkinson's disease and highlight the utility of applying convergent dimensionality reduction techniques to investigate complex clinical symptoms. In addition, the patterns we describe in Parkinson's disease converge with those seen in other conditions, suggesting that reduced hierarchical differentiation across sensory-perceptual systems may be a common transdiagnostic vulnerability in neuropsychiatric disorders with perceptual disturbances.

Whole-brain neuronal MCT2 lactate transporter expression links metabolism to human brain structure and function.

Brain activity is constrained by local availability of chemical energy, which is generated through compartmentalized metabolic processes. By analyzing data of whole human brain gene expression, we characterize the spatial distribution of seven glucose and monocarboxylate membrane transporters that mediate astrocyte-neuron lactate shuttle transfer of energy. We found that the gene coding for neuronal MCT2 is the only gene enriched in cerebral cortex where its abundance is inversely correlated with cortical thickness. Coexpression network analysis revealed that MCT2 was the only gene participating in an organized gene cluster enriched in K[Formula: see text] dynamics. Indeed, the expression of K[Formula: see text] subunits, which mediate lactate increases with spiking activity, is spatially coupled to MCT2 distribution. Notably, MCT2 expression correlated with fluorodeoxyglucose positron emission tomography task-dependent glucose utilization. Finally, the MCT2 messenger RNA gradient closely overlaps with functional MRI brain regions associated with attention, arousal, and stress. Our results highlight neuronal MCT2 lactate transporter as a key component of the cross-talk between astrocytes and neurons and a link between metabolism, cortical structure, and state-dependent brain function.

Recent Advances at the Interface of Neuroscience and Artificial Neural Networks.

Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural networks (ANNs) have exploited biological properties to solve complex problems. However, despite their effectiveness for specific tasks, ANNs are yet to realize the flexibility and adaptability of biological cognition. This review highlights recent advances in computational and experimental research to advance our understanding of biological and artificial intelligence. In particular, we discuss critical mechanisms from the cellular, systems, and cognitive neuroscience fields that have contributed to refining the architecture and training algorithms of ANNs. Additionally, we discuss how recent work used ANNs to understand complex neuronal correlates of cognition and to process high throughput behavioral data.

Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.

Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges.

Integrative Brain States Facilitate the Expression of Parkinson's Tremor.

BACKGROUND: Parkinson's disease (PD) rest tremor emerges from pathological activity in the basal ganglia and cerebello-thalamo-cortical circuits. A well-known clinical feature is the waxing and waning of PD tremor amplitude, but the mechanisms that drive this variability are unclear. Previous work has shown that arousal amplifies PD tremor by increasing between-network connectivity. Furthermore, brain states in PD are biased toward integration rather than segregation, a pattern that is also associated with increased arousal. OBJECTIVE: The aim was to test the hypothesis that fluctuations in integrative brain states and/or arousal drive spontaneous fluctuations in PD rest tremor. METHODS: We compared the temporal relationship between cerebral integration, the ascending arousal system, and tremor, both during cognitive load and in the resting state. In 40 tremor-dominant PD patients, we performed functional magnetic resonance imaging using concurrent tremor recordings and proxy measures of the ascending arousal system (pupil diameter, heart rate). We calculated whole-brain dynamic functional connectivity and used graph theory to determine a scan-by-scan measure of cerebral integration, which we related to the onset of tremor episodes. RESULTS: Fluctuations in cerebral integration were time locked to spontaneous changes in tremor amplitude: cerebral integration increased 13 seconds before tremor onset and predicted the amplitude of subsequent increases in tremor amplitude. During but not before tremor episodes, pupil diameter and heart rate increased and correlated with tremor amplitude. CONCLUSIONS: Integrative brain states are an important cerebral environment in which tremor-related activity emerges, which is then amplified by the ascending arousal system. New treatments focused on attenuating enhanced cerebral integration in PD may reduce tremor. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Subthalamic Nucleus Activity during Cognitive Load and Gait Dysfunction in Parkinson's Disease.

BACKGROUND: Gait freezing is a common, disabling symptom of Parkinson's disease characterized by sudden motor arrest during walking. Adaptive deep brain stimulation devices that detect freezing and deliver real-time, symptom-specific stimulation are a potential treatment strategy. Real-time alterations in subthalamic nucleus firing patterns have been demonstrated with lower limb freezing, however, whether similar abnormal signatures occur with freezing provoked by cognitive load, is unknown. METHODS: We obtained subthalamic nucleus microelectrode recordings from eight Parkinson's disease patients performing a validated virtual reality gait task, requiring responses to on-screen cognitive cues while maintaining motor output. RESULTS: Signal analysis during 15 trials containing freezing or significant motor output slowing precipitated by dual-tasking demonstrated reduced θ frequency (3-8 Hz) firing compared to 18 unaffected trials. CONCLUSIONS: These preliminary results reveal a potential neurobiological basis for the interplay between cognitive factors and gait disturbances including freezing in Parkinson's disease, informing development of adaptive deep brain stimulation protocols. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Understanding the effects of serotonin in the brain through its role in the gastrointestinal tract.

The neuromodulatory arousal system imbues the nervous system with the flexibility and robustness required to facilitate adaptive behaviour. While there are well understood mechanisms linking dopamine, noradrenaline and acetylcholine to distinct behavioural states, similar conclusions have not been as readily available for serotonin. Fascinatingly, despite clear links between serotonergic function and cognitive capacities as diverse as reward processing, exploration, and the psychedelic experience, over 95% of the serotonin in the body is released in the gastrointestinal tract, where it controls digestive muscle contractions (peristalsis). Here, we argue that framing neural serotonin as a rostral extension of the gastrointestinal serotonergic system dissolves much of the mystery associated with the central serotonergic system. Specifically, we outline that central serotonin activity mimics the effects of a digestion/satiety circuit mediated by hypothalamic control over descending serotonergic nuclei in the brainstem. We review commonalities and differences between these two circuits, with a focus on the heterogeneous expression of different classes of serotonin receptors in the brain. Much in the way that serotonin-induced peristalsis facilitates the work of digestion, serotonergic influences over cognition can be reframed as performing the work of cognition. Extending this analogy, we argue that the central serotonergic system allows the brain to arbitrate between different cognitive modes as a function of serotonergic tone: low activity facilitates cognitive automaticity, whereas higher activity helps to identify flexible solutions to problems, particularly if and when the initial responses fail. This perspective sheds light on otherwise disparate capacities mediated by serotonin, and also helps to understand why there are such pervasive links between serotonergic pathology and the symptoms of psychiatric disorders.

Priorities for research on neuromodulatory subcortical systems in Alzheimer's disease: Position paper from the NSS PIA of ISTAART.

The neuromodulatory subcortical system (NSS) nuclei are critical hubs for survival, hedonic tone, and homeostasis. Tau-associated NSS degeneration occurs early in Alzheimer's disease (AD) pathogenesis, long before the emergence of pathognomonic memory dysfunction and cortical lesions. Accumulating evidence supports the role of NSS dysfunction and degeneration in the behavioral and neuropsychiatric manifestations featured early in AD. Experimental studies even suggest that AD-associated NSS degeneration drives brain neuroinflammatory status and contributes to disease progression, including the exacerbation of cortical lesions. Given the important pathophysiologic and etiologic roles that involve the NSS in early AD stages, there is an urgent need to expand our understanding of the mechanisms underlying NSS vulnerability and more precisely detail the clinical progression of NSS changes in AD. Here, the NSS Professional Interest Area of the International Society to Advance Alzheimer's Research and Treatment highlights knowledge gaps about NSS within AD and provides recommendations for priorities specific to clinical research, biomarker development, modeling, and intervention. HIGHLIGHTS: Neuromodulatory nuclei degenerate in early Alzheimer's disease pathological stages. Alzheimer's pathophysiology is exacerbated by neuromodulatory nuclei degeneration. Neuromodulatory nuclei degeneration drives neuropsychiatric symptoms in dementia. Biomarkers of neuromodulatory integrity would be value-creating for dementia care. Neuromodulatory nuclei present strategic prospects for disease-modifying therapies.

The Contribution of Noradrenergic Activity to Anxiety-Induced Freezing of Gait.

BACKGROUND: Freezing of gait is a complex paroxysmal phenomenon that is associated with a variety of sensorimotor, cognitive and affective deficits, and significantly impacts quality of life in patients with Parkinson's disease (PD). Despite a growing body of evidence that suggests anxiety may be a crucial contributor to freezing of gait, no research study to date has investigated neural underpinnings of anxiety-induced freezing of gait. OBJECTIVE: Here, we aimed to investigate how anxiety-inducing contexts might "set the stage for freezing," through the ascending arousal system, by examining an anxiety-inducing virtual reality gait paradigm inside functional magnetic resonance imaging (fMRI). METHODS: We used a virtual reality gait paradigm that has been validated to elicit anxiety by having participants navigate a virtual plank, while simultaneously collecting task-based fMRI from individuals with idiopathic PD with confirmed freezing of gait. RESULTS: First, we established that the threatening condition provoked more freezing when compared to the non-threatening condition. By using a dynamic connectivity analysis, we identified patterns of increased "cross-talk" within and between motor, limbic, and cognitive networks in the threatening conditions. We established that the threatening condition was associated with heightened network integration. We confirmed the sympathetic nature of this phenomenon by demonstrating an increase in pupil dilation during the anxiety-inducing condition of the virtual reality gait paradigm in a secondary experiment. CONCLUSIONS: In conclusion, our findings represent a neurobiological mechanistic pathway through which heightened sympathetic arousal related to anxiety could foster increased "cross-talk" between distributed cortical networks that ultimately manifest as paroxysmal episodes of freezing of gait. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Hippocampal atrophy and intrinsic brain network dysfunction relate to alterations in mind wandering in neurodegeneration.

Mind wandering represents the human capacity for internally focused thought and relies upon the brain's default network and its interactions with attentional networks. Studies have characterized mind wandering in healthy people, yet there is limited understanding of how this capacity is affected in clinical populations. This paper used a validated thought-sampling task to probe mind wandering capacity in two neurodegenerative disorders: behavioral variant frontotemporal dementia [(bvFTD); n = 35] and Alzheimer's disease [(AD); n = 24], compared with older controls (n = 37). These patient groups were selected due to canonical structural and functional changes across sites of the default and frontoparietal networks and well-defined impairments in cognitive processes that support mind wandering. Relative to the controls, bvFTD patients displayed significantly reduced mind wandering capacity, offset by a significant increase in stimulus-bound thought. In contrast, AD patients demonstrated comparable levels of mind wandering to controls, in the context of a relatively subtle shift toward stimulus-/task-related forms of thought. In the patient groups, mind wandering was associated with gray matter integrity in the hippocampus/parahippocampus, striatum, insula, and orbitofrontal cortex. Resting-state functional connectivity revealed associations between mind wandering capacity and connectivity within and between regions of the frontoparietal and default networks with distinct patterns evident in patients vs. controls. These findings support a relationship between altered mind wandering capacity in neurodegenerative disorders and structural and functional integrity of the default and frontoparietal networks. This paper highlights a dimension of cognitive dysfunction not well documented in neurodegenerative disorders and validates current models of mind wandering in a clinical population.

Brain state kinematics and the trajectory of task performance improvement.

Dimensionality reduction techniques offer a unique perspective on brain state dynamics, in which systems-level activity can be tracked through the engagement of a small number of component trajectories. Used in combination with neuroimaging data collected during the performance of cognitive tasks, these approaches can expose the otherwise latent dimensions upon which the brain reconfigures in order to facilitate cognitive performance. Here, we utilized Principal Component Analysis to transform parcellated BOLD timeseries from an fMRI dataset in which 70 human subjects performed an instruction based visuomotor learning task into orthogonal low-dimensional components. We then used Linear Discriminant Analysis to maximise the mean differences between the low-dimensional signatures of fast-and-slow reaction times and early-and-late learners, while also conserving variance present within these groups. The resultant basis set allowed us to describe meaningful differences between these groups and, importantly, to detail the patterns of brain activity which underpin these differences. Our results demonstrate non-linear interactions between three key brain activation maps with convergent trajectories observed at higher task repetitions consistent with optimization. Furthermore, we show subjects with the greatest reaction time improvements have delayed recruitment of left dorsal and lateral prefrontal cortex, as well as deactivation in parts of the occipital lobe and motor cortex, and that the slowest performers have weaker recruitment of somatosensory association cortex and left ventral visual stream, as well as weaker deactivation in the dorsal lateral prefrontal cortex. Overall our results highlight the utility of a kinematic description of brain states, whereby reformatting data into low-dimensional trajectories sensitive to the subtleties of a task can capture non-linear trends in a tractable manner and permit hypothesis generation at the level of brain states.

Comparison of Locus Coeruleus Pathology with Nigral and Forebrain Pathology in Parkinson's Disease.

BACKGROUND: Pathology in the noradrenergic A6 locus coeruleus has not been compared with more rostral dopaminergic A9 substantia nigra and A10 ventral tegmental area, and cholinergic Ch4 basal nucleus and Ch1/2 septal regions in the same cases of Parkinson's disease (PD). OBJECTIVE: To determine whether there is a gradient of caudal to rostral cell loss in PD. METHODS: Postmortem brains were collected from longitudinally followed donors with PD (n = 14) and aged-matched healthy donors (n = 13), six with restricted brainstem Lewy pathology (RLP), fixed in formalin and serial tissue slabs processed for cell and pathological quantitation. Noradrenergic A6 neurons were assessed and compared with previously published midbrain and basal forebrain data. From these data, regression estimates of pathological onset and progression were determined. RESULTS: Restricted Lewy pathology (RLP) cases had high pathological variability but no significant reduction in neurons. Pathology containing A6 neuron loss started at PD diagnosis and progressed faster (2.4% p.a) than the loss of dopaminergic A9 neurons (2% loss p.a.). Cases with dementia had significantly more pathology in noradrenergic and cholinergic neurons, had greater noradrenergic A6 neuron loss (29% more, progressing at 3.2% p.a.), and a selective loss of lateral A10 nonmelanized dopamine-producing neurons (starting a decade following diagnosis). CONCLUSIONS: These findings show that in the same Parkinson's disease cases cell loss in these neurotransmitter systems does not follow a strict caudal to rostral trajectory and suggests symptom onset may relate to substantial pathology in the noradrenergic A6 locus coeruleus neurons in people with reduced dopamine-producing A9 substantia nigra neurons. © 2021 International Parkinson and Movement Disorder Society.

Cognitive fluctuations in Lewy body dementia: towards a pathophysiological framework.

Fluctuating cognition is a complex and disabling symptom that is seen most frequently in the context of Lewy body dementias encompassing dementia with Lewy bodies and Parkinson's disease dementia. In fact, since their description over three decades ago, cognitive fluctuations have remained a core diagnostic feature of dementia with Lewy bodies, the second most common dementia in the elderly. In the absence of reliable biomarkers for Lewy body pathology, the inclusion of such patients in therapeutic trials depends on the accurate identification of such core clinical features. Yet despite their diagnostic relevance, cognitive fluctuations remain poorly understood, in part due to the lack of a cohesive clinical and scientific explanation of the phenomenon itself. Motivated by this challenge, the present review examines the history, clinical phenomenology and assessment of cognitive fluctuations in the Lewy body dementias. Based on these data, the key neuropsychological, neurophysiological and neuroimaging correlates of cognitive fluctuations are described and integrated into a novel testable heuristic framework from which new insights may be gained.

The Human Intraparietal Sulcus Modulates Task-Evoked Functional Connectivity.

Past studies have demonstrated that flexible interactions between brain regions support a wide range of goal-directed behaviors. However, the neural mechanisms that underlie adaptive communication between brain regions are not well understood. In this study, we combined theta-burst transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging to investigate the sources of top-down biasing signals that influence task-evoked functional connectivity. Subjects viewed sequences of images of faces and buildings and were required to detect repetitions (2-back vs. 1-back) of the attended stimuli category (faces or buildings). We found that functional connectivity between ventral temporal cortex and the primary visual cortex (VC) increased during processing of task-relevant stimuli, especially during higher memory loads. Furthermore, the strength of functional connectivity was greater for correct trials. Increases in task-evoked functional connectivity strength were correlated with increases in activity in multiple frontal, parietal, and subcortical (caudate and thalamus) regions. Finally, we found that TMS to superior intraparietal sulcus (IPS), but not to primary somatosensory cortex, decreased task-specific modulation in connectivity patterns between the primary VC and the parahippocampal place area. These findings demonstrate that the human IPS is a source of top-down biasing signals that modulate task-evoked functional connectivity among task-relevant cortical regions.

Predictive Modeling of Brown Rust of Sugarcane Based on Temperature and Relative Humidity in Florida.

Logistic regression models were developed from 5 years (2014 to 2018) of disease severity and weather data in an attempt to predict brown rust of sugarcane at the Everglades Research and Education Center in Belle Glade, Florida. Disease severity (percentage area of the top visible dewlap leaf covered by rust) was visually assessed in the field every 2 weeks for two varieties susceptible to brown rust. A total of 250 variables were derived from weather data for 10- to 40-day periods before each brown rust assessment day. A subset of these variables were then evaluated as potential predictors of severity of brown rust based on their individual correlation or their biological meaningfulness. Analyses of correlation and stepwise logistic regression allowed us to identify afternoon humid thermal ratio (AHTR), temperature-based duration variables, and their interaction terms as the most significant variables associated with brown rust epidemics of sugarcane in Florida. The nine best predictive models were identified based on model accuracy, sensitivity, specificity, and estimates of the prediction error. The prediction accuracy of these models ranged from 73 to 85%. Single-variable model BR2 (based on AHTR) classified 89% of the epidemic and 81% of the nonepidemic status of the disease. More than 83% of the epidemics and 81% of the nonepidemic status of sugarcane brown rust was correctly classified via multiple-variable models. These models can be used as components of a rust disease warning system to assist in the management of brown rust epidemics of sugarcane in south Florida.

Core and matrix thalamic sub-populations relate to spatio-temporal cortical connectivity gradients.

Recent neuroimaging experiments have defined low-dimensional gradients of functional connectivity in the cerebral cortex that subserve a spectrum of capacities that span from sensation to cognition. Despite well-known anatomical connections to the cortex, the subcortical areas that support cortical functional organization have been relatively overlooked. One such structure is the thalamus, which maintains extensive anatomical and functional connections with the cerebral cortex across the cortical mantle. The thalamus has a heterogeneous cytoarchitecture, with at least two distinct cell classes that send differential projections to the cortex: granular-projecting 'Core' cells and supragranular-projecting 'Matrix' cells. Here we use high-resolution 7T resting-state fMRI data and the relative amount of two calcium-binding proteins, parvalbumin and calbindin, to infer the relative distribution of these two cell-types (Core and Matrix, respectively) in the thalamus. First, we demonstrate that thalamocortical connectivity recapitulates large-scale, low-dimensional connectivity gradients within the cerebral cortex. Next, we show that diffusely-projecting Matrix regions preferentially correlate with cortical regions with longer intrinsic fMRI timescales. We then show that the Core-Matrix architecture of the thalamus is important for understanding network topology in a manner that supports dynamic integration of signals distributed across the brain. Finally, we replicate our main results in a distinct 3T resting-state fMRI dataset. Linking molecular and functional neuroimaging data, our findings highlight the importance of the thalamic organization for understanding low-dimensional gradients of cortical connectivity.

Time-varying nodal measures with temporal community structure: A cautionary note to avoid misinterpretation.

In network neuroscience, temporal network models have gained popularity. In these models, network properties have been related to cognition and behavior. Here, we demonstrate that calculating nodal properties that are dependent on temporal community structure (such as the participation coefficient [PC]) in time-varying contexts can potentially lead to misleading results. Specifically, with regards to the participation coefficient, increases in integration can be inferred when the opposite is occurring. Further, we present a temporal extension to the PC measure (temporal PC) that circumnavigates this problem by jointly considering all community partitions assigned to a node through time. The proposed method allows us to track a node's integration through time while adjusting for the possible changes in the community structure of the overall network.