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Dynamic functional network connectivity (dFNC) is an expansion of static FNC (sFNC) that reflects connectivity variations among brain networks. This study aimed to investigate changes in sFNC and dFNC strength and temporal properties in individuals with subthreshold depression (StD). Forty-two individuals with subthreshold depression and 38 healthy controls (HCs) were included in this study. Group independent component analysis (GICA) was used to determine target resting-state networks, namely, executive control network (ECN), default mode network (DMN), sensorimotor network (SMN) and dorsal attentional network (DAN). Sliding window and k-means clustering analyses were used to identify dFNC patterns and temporal properties in each subject. We compared sFNC and dFNC differences between the StD and HCs groups. Relationships between changes in FNC strength, temporal properties, and neurophysiological score were evaluated by Spearman's correlation analysis. The sFNC analysis revealed decreased FNC strength in StD individuals, including the DMN-CEN, DMN-SMN, SMN-CEN, and SMN-DAN. In the dFNC analysis, 4 reoccurring FNC patterns were identified. Compared to HCs, individuals with StD had increased mean dwell time and fraction time in a weakly connected state (state 4), which is associated with self-focused thinking status. In addition, the StD group demonstrated decreased dFNC strength between the DMN-DAN in state 2. sFNC strength (DMN-ECN) and temporal properties were correlated with HAMD-17 score in StD individuals (all p < 0.01). Our study provides new evidence on aberrant time-varying brain activity and large-scale network interaction disruptions in StD individuals, which may provide novel insight to better understand the underlying neuropathological mechanisms.

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Suicide in youth and young adults is a serious public health problem. However, the biological mechanisms of suicidal ideation (SI) remain poorly understood. The primary goal of these analyses was to identify the connectome profile of suicidal ideation using resting state electroencephalography (EEG). We evaluated the neurocircuitry of SI in a sample of youths and young adults (aged 10-26 years, n = 111) with current or past diagnoses of either a depressive disorder or bipolar disorder who were enrolled in the Texas Resilience Against Depression Study (T-RAD). Neurocircuitry was analyzed using orthogonalized power envelope connectivity computed from resting state EEG. Suicidal ideation was assessed with the 3-item Suicidal Thoughts factor of the Concise Health Risk Tracking self-report scale. The statistical pipeline involved dimension reduction using principal component analysis, and the association of neuroimaging data with SI using regularized canonical correlation analysis. From the original 111 participants and the correlation matrix of 4950 EEG connectivity pairs in each band (alpha, beta, theta), dimension reduction generated 1305 EEG connectivity pairs in the theta band, 2337 EEG pairs in the alpha band, and 914 EEG connectivity pairs in the beta band. Overall, SI was consistently involved with dysfunction of the default mode network (DMN). This report provides preliminary evidence of DMN dysfunction associated with active suicidal ideation in adolescents. Using EEG using power envelopes to compute connectivity moves us closer to using neurocircuit dysfunction in the clinical setting to identify suicidal ideation.

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PURPOSE: Memory is one of the main specific cognitive domains impaired with attention and processing speed after a pediatric brain tumor. This work explored the long-term impact of radiotherapy in children with posterior fossa tumor (PFT) on brain connectivity in neural circuits involved in memory using resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: A total of 20 irradiated and 15 non-irradiated PFT survivors, and 21 healthy controls, prospectively included in the IMPALA study (NCT04324450), performed memory tests assessing episodic, procedural, and working memories and were subjected to an rs-fMRI. We manually contoured main structures involved in memory to explore connectivity at rest in a seed-to-voxel analysis. The groups were compared and differences in connectivity were correlated with behavioral scores and irradiation doses. RESULTS: The performance of all mnesic tasks was lower in PFT survivors with a greater alteration in working and episodic memory in irradiated patients. Irradiated survivors had atypical connectivities in all memory circuits compared to controls and in cortico-caudate and cortico-cerebellar circuits compared to non-irradiated survivors. Non-irradiated survivors had only atypical connectivities in the cortico-cerebellar circuits compared to controls. In irradiated survivors, atypical connectivities in cortico-hippocampal circuits were linked with episodic memory scores and dose of irradiation to the left hippocampus and in cortico-striatal circuits with procedural memory scores and dose of irradiation to the striatum. CONCLUSION: The results of this study highlight that irradiation has a long-term impact on brain connectivity in brain circuits involved in memory after pediatric PFT with a specific radiation-dose effect in supratentorial structures.

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The purpose of this study was to explore the value of resting-state magnetic resonance imaging (MRI) based on the brain extraction tool (BET) algorithm in evaluating the cranial nerve function of patients with delirium in intensive care unit (ICU). A total of 100 patients with delirium in hospital were studied, and 20 healthy volunteers were used as control. All the subjects were examined by MRI, and the images were analyzed by the BET algorithm, and the convolution neural network (CNN) algorithm was introduced for comparison. The application effects of the two algorithms were analyzed, and the differences of brain nerve function between delirium patients and normal people were explored. The results showed that the root mean square error, high frequency error norm, and structural similarity of the BET algorithm were 70.4%, 71.5%, and 0.92, respectively, which were significantly higher than those of the CNN algorithm (P < 0.05). Compared with normal people, the ReHo values of pontine, hippocampus (right), cerebellum (left), midbrain, and basal ganglia in delirium patients were significantly higher. ReHo values of frontal gyrus, middle frontal gyrus, left inferior frontal gyrus, parietal lobe, and temporal lobe and anisotropy scores (FA) of cerebellums (left), frontal lobe, temporal lobe (left), corpus callosum, and hippocampus (left) decreased significantly. The average diffusivity (MD) of medial frontal lobe, superior temporal gyrus (right), the first half of cingulate gyrus, bilateral insula, and caudate nucleus (left) increased significantly (P < 0.05). MRI based on the deep learning algorithm can effectively improve the image quality, which is valuable in evaluating the brain nerve function of delirium patients. Abnormal brain structure damage and abnormal function can be used to help diagnose delirium.

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Narrowing down the histopathological changes in the brain after early-life exposure to general anaesthesia has presented a consistent challenge for preclinical models of anaesthetic neurotoxicity. Using resting-state functional magnetic resonance imaging, in this issue of the journal Neudecker and colleagues demonstrated in vivo connectivity changes in the brain following a seed-based analysis that was derived from previously reported histopathology in the same animals. The combination of neurohistology and neuroimaging should help focus future preclinical studies investigating the developmental consequences of early exposure to general anaesthesia.

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Emotional dysregulation symptoms following a concussion are associated with an increased risk for emotional dysregulation disorders (e.g., depression and anxiety), especially in adolescents. However, predicting the emergence or worsening of emotional dysregulation symptoms after concussion and the extent to which this predates the onset of subsequent psychiatric morbidity after injury remains challenging. Although advanced neuroimaging techniques, such as functional magnetic resonance imaging and diffusion magnetic resonance imaging, have been used to detect and monitor concussion-related brain abnormalities in research settings, their clinical utility remains limited. In this narrative review, we have performed a comprehensive search of the available literature regarding emotional regulation, adolescent concussion, and advanced neuroimaging techniques in electronic databases (PubMed, Scopus, and Google Scholar). We highlight clinical evidence showing the heightened susceptibility of adolescents to experiencing emotional dysregulation symptoms following a concussion. Furthermore, we describe and provide empirical support for widely used magnetic resonance imaging modalities (i.e., functional and diffusion imaging), which are utilized to detect abnormalities in circuits responsible for emotional regulation. Additionally, we assess how these abnormalities relate to the emotional dysregulation symptoms often reported by adolescents post-injury. Yet, it remains to be determined if a progression of concussion-related abnormalities exists, especially in brain regions that undergo significant developmental changes during adolescence. We conclude that neuroimaging techniques hold potential as clinically useful tools for predicting and, ultimately, monitoring the treatment response to emotional dysregulation in adolescents following a concussion.

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At the time of the start of Biological Psychology cognitive studies had developed approaches to measuring cognitive processes. However, linking these to the underlying biology in the typical human brain had hardly begun. A critical step came in 1988 when methods for imaging the human brain in cognitive tasks began. By 1990 it was possible to describe three brain networks that carried out the hypothesized cognitive functions outlined 20 years before. Their development was traced in infancy, first using age-appropriate tasks and later through resting state imaging. Imaging was applied to both voluntary and involuntary cued shifts of visual orienting in humans and primates, and a summary was presented in 2002. By 2008 these new imaging findings were used to test hypotheses about the genes involved in each network. Recently, studies of mice using optogenetics to control populations of neurons have brought us closer to a synthesis of how attention and memory networks operate together in human learning. Perhaps the coming years will bring us to an integrated theory of aspects of attention using data from all the levels that can illuminate these issues, thus fulfilling a key goal of the Journal.

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Spontaneous thought is an adaptive cognitive process that can produce novel and insightful thought sequences useful in guiding future behavior. In many psychiatric disorders, spontaneous thinking becomes intrusive and uncontrolled, and can trigger symptoms such as craving, repetitive negative thinking and trauma-related memories. We link studies using clinical imaging and rodent modeling towards understanding the neurocircuitry and neuroplasticity of intrusive thinking. We propose a framework in which drugs or stress change the homeostatic set point of brain reward circuitry, which then impacts subsequent plasticity induced by drug/stress conditioned cues (metaplastic allostasis). We further argue for the importance of examining not only the canonical pre- and postsynapse, but also the adjacent astroglial protrusions and extracellular matrix that together form the tetrapartite synapse and that plasticity throughout the tetrapartite synapse is necessary for cue-induced drug or stress behaviors. This analysis reveals that drug use or trauma cause long-lasting allostatic brain plasticity that sets the stage for subsequent drug/trauma-associated cues to induce transient plasticity that can lead to intrusive thinking.

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BACKGROUND & OBJECTIVE: We have previously identified aberrant connectivity of the left precuneus, ventrolateral prefrontal cortex, anterior cingulate cortex, and anterior insula in patients with either a paranoid (schizophrenia), or a depressive syndrome (both unipolar and bipolar). In the current study, we attempted to replicate and expand these findings by including a healthy control sample and separating the patients in a depressive episode into two groups: unipolar and bipolar depression. We hypothesized that the connections between those major nodes of the resting state networks would demonstrate different patterns in the three patient groups compared to the healthy subjects. METHODS: Resting-state functional MRI was performed on a sample of 101 participants, of which 26 patients with schizophrenia (current psychotic episodes), 24 subjects with Bipolar Disorder (BD), 33 with Major Depressive Disorder (MDD) (both BD and MDD patients were in a current depressive episode), and 21 healthy controls. Spectral Dynamic Causal Modeling was used to calculate the coupling values between eight regions of interest, including the anterior precuneus (PRC), anterior hippocampus, anterior insula, angular gyrus, lateral Orbitofrontal Cortex (OFC), middle frontal gyrus, planum temporale, and anterior thalamus. RESULTS & CONCLUSION: We identified disturbed effective connectivity from the left lateral orbitofrontal cortex to the left anterior precuneus that differed significantly between unipolar depression, where the influence was inhibitory, and bipolar depression, where the effect was excitatory. A logistic regression analysis correctly classified 75% of patients with unipolar and bipolar depression based solely on the coupling values of this connection. In addition, patients with schizophrenia demonstrated negative effective connectivity from the anterior PRC to the lateral OFC, which distinguished them from healthy controls and patients with major depression. Future studies with unmedicated patients will be needed to establish the replicability of our findings.

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Increasing evidence demonstrates that environmental factors meaningfully impact the development of the brain (Hyde et al., 2020; McEwen and Akil, 2020). Recent work from the Adolescent Brain Cognitive Development (ABCD) Study® suggests that puberty may indirectly account for some association between the family environment and brain structure and function (Thijssen et al., 2020). However, a limited number of large studies have evaluated what, how, and why environmental factors impact neurodevelopment. When these topics are investigated, there is typically inconsistent operationalization of variables between studies which may be measuring different aspects of the environment and thus different associations in the analytic models. Multiverse analyses (Steegen et al., 2016) are an efficacious technique for investigating the effect of different operationalizations of the same construct on underlying interpretations. While one of the assets of Thijssen et al. (2020) was its large sample from the ABCD data, the authors used an early release that contained 38% of the full ABCD sample. Then, the analyses used several 'researcher degrees of freedom' (Gelman and Loken, 2014) to operationalize key independent, mediating and dependent variables, including but not limited to, the use of a latent factor of preadolescents' environment comprised of different subfactors, such as parental monitoring and child-reported family conflict. While latent factors can improve reliability of constructs, the nuances of each subfactor and measure that comprise the environment may be lost, making the latent factors difficult to interpret in the context of individual differences. This study extends the work of Thijssen et al. (2020) by evaluating the extent to which the analytic choices in their study affected their conclusions. In Aim 1, using the same variables and models, we replicate findings from the original study using the full sample in Release 3.0. Then, in Aim 2, using a multiverse analysis we extend findings by considering nine alternative operationalizations of family environment, three of puberty, and five of brain measures (total of 135 models) to evaluate the impact on conclusions from Aim 1. In these results, 90% of the directions of effects and 60% of the p-values (e.g. p > .05 and p < .05) across effects were comparable between the two studies. However, raters agreed that only 60% of the effects had replicated. Across the multiverse analyses, there was a degree of variability in beta estimates across the environmental variables, and lack of consensus between parent reported and child reported pubertal development for the indirect effects. This study demonstrates the challenge in defining which effects replicate, the nuance across environmental variables in the ABCD data, and the lack of consensus across parent and child reported puberty scales in youth.

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Objective: Type 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and topological properties among whole brain networks and correlations with impaired cognition and distinguish T2DM from healthy controls (HC) to identify potential biomarkers for cognition abnormalities. Methods: A total of 80 T2DM and 55 well-matched HC were recruited in this study. Subjects' clinical data, neuropsychological tests and resting-state functional magnetic resonance imaging data were acquired. Whole-brain network FC were mapped, the topological characteristics were analyzed using a graph-theoretic approach, the FC and topological characteristics of the network were compared between T2DM and HC using a general linear model, and correlations between networks and clinical and cognitive characteristics were identified. The support vector machine (SVM) model was used to identify differences between T2DM and HC. Results: In patients with T2DM, FC was higher in two core regions [precuneus/posterior cingulated cortex (PCC)_1 and later prefrontal cortex_1] in the default mode network and lower in bilateral superior parietal lobes (within dorsal attention network), and decreased between the right medial frontal cortex and left auditory cortex. The FC of the right frontal medial-left auditory cortex was positively correlated with the Montreal Cognitive Assessment scales and negatively correlated with the blood glucose levels. Long-range connectivity between bilateral auditory cortex was missing in the T2DM. The nodal degree centrality and efficiency of PCC were higher in T2DM than in HC (P < 0.005). The nodal degree centrality in the PCC in the SVM model was 97.56% accurate in distinguishing T2DM patients from HC, demonstrating the reliability of the prediction model. Conclusion: Functional abnormalities in the auditory cortex in T2DM may be related to cognitive impairment, such as memory and attention, and nodal degree centrality in the PCC might serve as a potential neuroimaging biomarker to predict and identify T2DM.

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BACKGROUND: Cognitive impairment is a hallmark of cerebral small vessel disease (cSVD). Functional magnetic resonance imaging has highlighted connections between patterns of brain activity and variability in behavior. We aimed to characterize the associations between imaging markers of cSVD, dynamic connectivity, and cognitive impairment. METHODS: We obtained magnetic resonance imaging and clinical data from the population-based Hamburg City Health Study. cSVD was quantified by white matter hyperintensities and peak-width of skeletonized mean diffusivity (PSMD). Resting-state blood oxygen level-dependent signals were clustered into discrete brain states, for which fractional occupancies (%) and dwell times (seconds) were computed. Cognition in multiple domains was assessed using validated tests. Regression analysis was used to quantify associations between white matter damage, spatial coactivation patterns, and cognitive function. RESULTS: Data were available for 979 participants (ages 45-74 years, median white matter hyperintensity volume 0.96 mL). Clustering identified five brain states with the most time spent in states characterized by activation (+) or suppression (-) of the default mode network (DMN) (fractional occupancy: DMN+ = 25.1 ± 7.2%, DMN- = 25.5 ± 7.2%). Every 4.7-fold increase in white matter hyperintensity volume was associated with a 0.95-times reduction of the odds of occupying DMN+ or DMN-. Time spent in DMN-related brain states was associated with executive function. CONCLUSIONS: Associations between white matter damage, whole-brain spatial coactivation patterns, and cognition suggest equalization of time spent in different brain states as a marker for cSVD-associated cognitive decline. Reduced gradients between brain states in association with brain damage and cognitive impairment reflect the dedifferentiation hypothesis of neurocognitive aging in a network-theoretical context.

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INTRODUCTION: Neurofibromatosis type 1 (NF1) is considered a model of neurodevelopmental disorder because of the high frequency of learning deficits, especially developmental coordination disorder. In neurodevelopmental disorder, Nicolson and Fawcett formulated the hypothesis of an impaired procedural learning system that has its origins in cortico-subcortical circuits. Our aim was to investigate the relationship between cortico-striatal connectivity and procedural perceptual-motor learning performance and motor skills in NF1 children. METHODS: Seventeen NF1 and 18 typically developing children aged between 8 and 12 years old participated in the study. All were right-handed and did not present intellectual or attention deficits. In all children, procedural perceptual-motor learning was assessed using a bimanual visuo-spatial serial reaction time task (SRTT) and motor skills using the Movement Assessment Battery for Children (M-ABC). All participants underwent a resting-state functional MRI session. We used a seed-based approach to explore cortico-striatal connectivity in somatomotor and frontoparietal networks. A comparison between the groups' striato-cortical connectivity and correlations between connectivity and learning (SRTT) and motor skills (M-ABC) were performed. RESULTS: At the behavioral level, SRTT scores are not significantly different in NF1 children compared to controls. However, M-ABC scores are significantly impaired within 9 patients (scores below the 15th percentile). At the cerebral level, NF1 children present a higher connectivity in the cortico-striatal regions mapping onto the right angular gyrus compared to controls. We found that the higher the connectivity values between these regions, differentiating NF1 and controls, the lower the M-ABC scores in the whole sample. No correlation was found for the SRTT scores. CONCLUSION: NF1 children present atypical hyperconnectivity in cortico-striatal connections. The relationship with motor skills could suggest a sensorimotor dysfunction already found in children with developmental coordination disorder. These abnormalities are not linked to procedural perceptual-motor learning assessed by SRTT.

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Dynamic functional network connectivity (dFNC) analysis is a widely used approach for capturing brain activation patterns, connectivity states, and network organization. However, a typical sliding window plus clustering (SWC) approach for analyzing dFNC models the system through a fixed sequence of connectivity states. SWC assumes connectivity patterns span throughout the brain, but they are relatively spatially constrained and temporally short-lived in practice. Thus, SWC is neither designed to capture transient dynamic changes nor heterogeneity across subjects/time. We propose a state-space time series summarization framework called "statelets" to address these shortcomings. It models functional connectivity dynamics at fine-grained timescales, adapting time series motifs to changes in connectivity strength, and constructs a concise yet informative representation of the original data that conveys easily comprehensible information about the phenotypes. We leverage the earth mover distance in a nonstandard way to handle scale differences and utilize kernel density estimation to build a probability density profile for local motifs. We apply the framework to study dFNC of patients with schizophrenia (SZ) and healthy control (HC). Results demonstrate SZ subjects exhibit reduced modularity in their brain network organization relative to HC. Statelets in the HC group show an increased recurrence across the dFNC time-course compared to the SZ. Analyzing the consistency of the connections across time reveals significant differences within visual, sensorimotor, and default mode regions where HC subjects show higher consistency than SZ. The introduced approach also enables handling dynamic information in cross-modal and multimodal applications to study healthy and disordered brains.

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Advancements in neuroimaging and the availability of large-scale datasets enable the use of more sophisticated machine learning algorithms. In this chapter, we non-exhaustively discuss relevant analytical steps for the analysis of neuroimaging data using machine learning (ML), while the field of radiomics will be addressed separately (c.f., Chap. 18 -Radiomics). Broadly classified into supervised and unsupervised approaches, we discuss the encoding/decoding framework, which is often applied in cognitive neuroscience, and the use of ML for the analysis of unlabeled data using clustering.

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The functional connectome fingerprint is a cluster of individualized brain functional connectivity patterns that are capable of distinguishing one individual from others. Although its existence has been demonstrated in adolescents and adults, whether such individualized patterns exist during infancy is barely investigated despite its importance in identifying the origin of the intrinsic connectome patterns that potentially mirror distinct behavioral phenotypes. To fill this knowledge gap, capitalizing on a longitudinal high-resolution structural and resting-state functional MRI dataset with 104 human infants (53 females) with 806 longitudinal scans (age, 16-876 d) and infant-specific functional parcellation maps, we observe that the brain functional connectome fingerprint may exist since infancy and keeps stable over months during early brain development. Specifically, we achieve an ∼78% individual identification rate by using ∼5% selected functional connections, compared with the best identification rate of 60% without connection selection. The frontoparietal networks recognized as the most contributive networks in adult functional connectome fingerprinting retain their superiority in infants despite being widely acknowledged as rapidly developing systems during childhood. The existence and stability of the functional connectome fingerprint are further validated on adjacent age groups. Moreover, we show that the infant frontoparietal networks can reach similar accuracy in predicting individual early learning composite scores as the whole-brain connectome, again resembling the observations in adults and highlighting the relevance of functional connectome fingerprint to cognitive performance. For the first time, these results suggest that each individual may retain a unique and stable marker of functional connectome during early brain development.SIGNIFICANCE STATEMENT Functional connectome fingerprinting during infancy featuring rapid brain development remains almost uninvestigated even though it is essential for understanding the early individual-level intrinsic pattern of functional organization and its relationship with distinct behavioral phenotypes. With an infant-tailored functional connection selection and validation strategy, we strive to provide the delineation of the infant functional connectome fingerprint by examining its existence, stability, and relationship with early cognitive performance. We observe that the brain functional connectome fingerprint may exist since early infancy and remains stable over months during the first 2 years. The identified key contributive functional connections and networks for fingerprinting are also verified to be highly predictive for cognitive score prediction, which reveals the association between infant connectome fingerprint and cognitive performance.

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Light plays a critical role in regulating physiology and behavior, including both visual and non-visual responses. In mammals, loss of both eyes abolishes all of these responses, demonstrating that the photoreceptors involved are exclusively ocular. By contrast, many non-mammalian species possess extra-ocular photoreceptors located in the pineal complex and deep brain. Whilst there have been suggestions of extra-ocular photoreception in mammals, including man, evidence for these photoreceptors is limited. One approach to objectively determine the presence of such receptors is to measure brain responses to light using functional magnetic resonance imaging (fMRI). Moreover, by using participants who are clinically anophthalmic (congenital and acquired), it is possible to investigate potential light detection in the absence of the retina. Here we scanned participants with anophthalmia and sighted participants in 4 different conditions; the first 3 conditions had a bright light source applied to the following locations: behind the right ear ("ear"), just below the nasal bridge and between the eyes ("head"), and at the right popliteal fossa ("knee"). In the fourth and final scan, the light source was switched off so that there was no light stimulus. All participants were scanned in a completely dark room. No consistent brain activity was detected during any of the light conditions in either sighted controls or anophthalmic participants. Thus, we do not provide any evidence for the presence of extraocular photoreceptors modulating human brain activity, despite recent evidence for gene transcription that may occur as a result of these photoreceptors.

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Studies have documented behavior differences between more versus less resilient adults with chronic pain (CP), but the presence and nature of underlying neurophysiological differences have received scant attention. In this study, we attempted to identify regions of interest (ROIs) in which resting state (Rs) brain activity discriminated more from less resilient CP subgroups based on multiple kernel learning (MKL). More and less resilient community-dwellers with chronic musculoskeletal pain (70 women, 39 men) engaged in structural and functional magnetic resonance imaging (MRI) scans, wherein MKL assessed Rs activity based on amplitude of low frequency fluctuations (ALFF), fractional amplitudes of low frequency fluctuations (fALFF), and regional homogeneity (ReHo) modalities to identify ROIs most salient for discriminating more versus less resilient subgroups. Compared to classification based on single modalities, multi-modal classification based on combined fALFF and ReHo features achieved a substantially higher classification accuracy rate (79%). Brain regions with the best discriminative power included those implicated in pain processing, reward, executive function, goal-directed action, emotion regulation and resilience to mood disorders though variation trends were not consistent between more and less resilient subgroups. Results revealed patterns of Rs activity that serve as possible biomarkers for resilience to chronic musculoskeletal pain.

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RATIONALE: Donepezil is a potent, noncompetitive, reversible, clinically effective acetylcholinesterase inhibitor. The effects of this drug on healthy brains have seldom been investigated. OBJECTIVES: The primary objective of the present study was to identify possible functional connectivity markers of the effect of donepezil in healthy young adult volunteers. METHODS: The study had a double-blind, randomized, crossover design. 30 healthy adult volunteers underwent resting-state MRI scans during 15 days of donepezil or placebo treatment, in accordance with the design. RESULTS: Results showed significant differences in intrinsic functional connectivity between donepezil and placebo, mainly in the right executive control network (RECN). More specifically, we found a decrease in the connectivity of the right inferior parietal node with other RECN nodes. Analysis using the cingulate cortex and parahippocampal regions as seeds also revealed complex modulation of functional connectivity in the donepezil condition. CONCLUSIONS: In conclusion, donepezil treatment for 15 days may result in reorganization of resting-state networks, compared with placebo.

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BACKGROUND AND PURPOSE: Magnetic resonance (MR) biomarkers are emerging for sports-related traumatic brain injury (TBI), but the effect of play time has not been characterized. Our goal was to characterize brain and inflammatory marker changes as a function of play time. METHODS: Nine male players (21±2 years old) from a single collegiate basketball team were included. MR imaging (MRI), MR spectroscopy, and plasma were collected pre, mid, and postseason. Game time played was calculated for each subject. Changes in brain volume, diffusion tensor imaging (DTI), metabolites (normalized to total creatine, tCr), temperature, structural and functional connectivity, and inflammatory markers were quantified. RESULTS: Myo-inositol/tCr in the left frontal white matter and brain temperature in the left frontal lobe varied significantly between time points. Glutamate (Glu/tCr) in the right frontal white matter and N-acetylaspartate in the posterior cingulate cortex (PCC) were negatively associated with minutes played. Midseason play time was associated with stronger blood-oxygen-level-dependent correlations between PCC and occipital areas, and weaker correlations between PCC and superior frontal connectivity. PCC Glu/tCr was positively associated with connectivity between the PCC and posterior supramarginal gyrus at preseason and with connectivity across time points among several right hemisphere regions. Volume, DTI, and inflammatory markers did not vary significantly. CONCLUSION: Given that MR parameters vary with game play time in the absence of diagnosed injury, play time should be considered as a factor in sports-related TBI research.

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