Altered static and dynamic functional network connectivity in individuals with subthreshold depression: a large-scale resting-state fMRI study.

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.

Existence of Functional Connectome Fingerprint during Infancy and Its Stability over Months.

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.

Needle in a haystack: localising the long-term neuronal changes from early-life exposure to general anaesthesia.

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.

Statelets: Capturing recurrent transient variations in dynamic functional network connectivity.

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.

Global Network Organization of the Fetal Functional Connectome.

Recent advances in brain imaging have enabled non-invasive in vivo assessment of the fetal brain. Characterizing brain development in healthy fetuses provides baseline measures for identifying deviations in brain function in high-risk clinical groups. We examined 110 resting state MRI data sets from fetuses at 19 to 40 weeks' gestation. Using graph-theoretic techniques, we characterized global organizational features of the fetal functional connectome and their prenatal trajectories. Topological features related to network integration (i.e., global efficiency) and segregation (i.e., clustering) were assessed. Fetal networks exhibited small-world topology, showing high clustering and short average path length relative to reference networks. Likewise, fetal networks' quantitative small world indices met criteria for small-worldness (σ > 1, ω = [-0.5 0.5]). Along with this, fetal networks demonstrated global and local efficiency, economy, and modularity. A right-tailed degree distribution, suggesting the presence of central areas that are more highly connected to other regions, was also observed. Metrics, however, were not static during gestation; measures associated with segregation-local efficiency and modularity-decreased with advancing gestational age. Altogether, these suggest that the neural circuitry underpinning the brain's ability to segregate and integrate information exists as early as the late 2nd trimester of pregnancy and reorganizes during the prenatal period. Significance statement. Mounting evidence for the fetal origins of some neurodevelopmental disorders underscores the importance of identifying features of healthy fetal brain functional development. Alterations in prenatal brain connectomics may serve as early markers for identifying fetal-onset neurodevelopmental disorders, which in turn provide improved surveillance of at-risk fetuses and support the initiation of early interventions.

Introduction to Machine Learning in Neuroimaging.

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.

Functional brain connectivity in ex utero premature infants compared to in utero fetuses.

Brain structural changes in premature infants appear before term age. Functional differences between premature infants and healthy fetuses during this period have yet to be explored. Here, we examined brain connectivity using resting state functional MRI in 25 very premature infants (VPT; gestational age at birth <32 weeks) and 25 healthy fetuses with structurally normal brain MRIs. Resting state data were evaluated using seed-based correlation analysis and network-based statistics using 23 regions of interest (ROIs) per hemisphere. Functional connectivity strength, the Pearson correlation between blood oxygenation level dependent signals over time across all ROIs, was compared between groups. In both cohorts, connectivity between homotopic ROIs showed a decreasing medial to lateral gradient. The cingulate cortex, medial temporal lobe and the basal ganglia shared the strongest connections. In premature infants, connections involving superior temporal, hippocampal, and occipital areas, among others, were stronger compared to fetuses. Premature infants showed stronger connectivity in sensory input and stress-related areas suggesting that extra-uterine environment exposure alters the development of select neural networks in the absence of structural brain injury.

Altered Functional Connectivity of Hypothalamus in Lifelong Premature Ejaculation Patients.

BACKGROUND: As one of the most prevalent sexual dysfunctions in men, lifelong premature ejaculation (PE) often leads to patient distress. The hypothalamus is implicated in the ejaculatory control of healthy males. However, we do not know whether the hypothalamus-related intrinsic connectivity is altered in lifelong PE patients. PURPOSE: To investigate abnormal intrinsic connectivity of the hypothalamus in lifelong PE patients compared with healthy controls (HCs). STUDY TYPE: Prospective pilot study using cross-sectional data of patients and HCs. SUBJECTS: Forty-seven lifelong PE patients and 30 HCs were included in this study. FIELD STRENGTH/SEQUENCE: 3.0T MRI scanner for T1 -weighted imaging using spoiled gradient recalled echo sequence and functional imaging using a single-shot gradient recalled echo sequence. ASSESSMENT: Preprocessing of MRI data and hypothalamus-seeded functional connectivity (FC) computation were performed using DPABI4.1. STATISTICAL TESTS: The two-sample t-test within SPM12 was adopted to examine possible alterations of intrinsic connectivity of hypothalamus in lifelong PE patients compared with HCs including anxiety and depression scores as covariates (false discovery rate-corrected, P < 0.05). The correlation analysis was then used to assess possible associations between the imaging findings and clinical features in the patient group (Bonferroni-corrected, P < 0.05). RESULTS: Compared with HCs, lifelong PE patients had decreased hypothalamus-seeded FC in the left orbitofrontal cortex, bilateral insula, superior temporal cortex, superior temporal pole, middle temporal cortex, left fusiform, right parahippocampal gyrus, and right cerebellum. The intravaginal ejaculatory latency time positively correlated with the mean z-score from the hypothalamus-insula (r = 0.45) and hypothalamus-cerebellum (r = 0.48) intrinsic connectivity, separately. DATA CONCLUSION: We have shown that hypothalamus-seeded FC alterations and the correlations between the aforementioned abnormal FC alterations and intravaginal ejaculatory latency time. The current findings may promote the understanding of the hypothalamus-related neural mechanisms involved in the abnormal ejaculatory information processing in lifelong PE patients. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 3 J. Magn. Reson. Imaging 2020;52:778-784.

Thalamic low frequency activity facilitates resting-state cortical interhemispheric MRI functional connectivity.

Blood-oxygen-level-dependent (BOLD) resting-state functional MRI (rsfMRI) has emerged as a valuable tool to map complex brain-wide functional networks, predict cognitive performance and identify biomarkers for neurological diseases. However, interpreting these findings poses challenges, as the neural basis of rsfMRI connectivity remains poorly understood. The thalamus serves as a relay station and modulates diverse long-range cortical functional integrations, yet few studies directly interrogate its role in brain-wide rsfMRI connectivity. Utilizing a multi-modal approach of rsfMRI, optogenetic stimulation and multi-depth cortical electrophysiology recording, we examined whether and how the somatosensory thalamus contributes to cortical interhemispheric rsfMRI connectivity. We found that low frequency (1 Hz) optogenetic stimulation of somatosensory-specific ventral posteromedial (VPM) thalamocortical excitatory neurons increased the interhemispheric rsfMRI connectivity in all examined sensory cortices, somatosensory, visual and auditory, and the local intrahemispheric BOLD activity at infraslow frequency (0.01-0.1 Hz). In parallel, multi-depth local field potential recordings at bilateral primary somatosensory cortices revealed increased interhemispheric correlations of low frequency neural oscillations (i.e., mainly < 10 Hz) at all cortical layers. Meanwhile, pharmacologically inhibiting VPM thalamocortical neurons decreased interhemispheric rsfMRI connectivity and local intrahemispheric infraslow BOLD activity in all sensory cortices. Taken together, our findings demonstrate that low frequency activities in the thalamo-cortical network contribute to brain-wide rsfMRI connectivity, highlighting the thalamus as a pivotal region that underlies rsfMRI connectivity.

Modulating functional connectivity between medial frontopolar cortex and amygdala by inhibitory and excitatory transcranial magnetic stimulation.

The prefrontal-limbic network in the human brain plays a major role in social cognition, especially cognitive control of emotion. The medial frontopolar cortex (mFP; Brodmann Area 10) and the amygdala are part of this network and display correlated neuronal activity in time, as measured by functional magnetic resonance imaging (fMRI). This functional connectivity is dynamic, sensitive to training, and affected in mental disorders. However, the effects of neurostimulation on functional connectivity within this network have not yet been systematically investigated. Here, we investigate the effects of both low- and high-frequency repetitive transcranial magnetic stimulation (rTMS) to the right mFP on functional connectivity between mFP and amygdala, as measured with resting state fMRI (rsfMRI). Three groups of healthy participants received either low-frequency rTMS (1 Hz; N = 18), sham TMS (1 Hz, subthreshold; N = 18) or high-frequency rTMS (20 Hz; N = 19). rsfMRI was acquired before and after (separate days). We hypothesized a modulation of functional connectivity in opposite directions compared to sham TMS through adjustment of the stimulation frequency. Groups differed in functional connectivity between mFP and amygdala after stimulation compared to before stimulation (low-frequency: decrease, high-frequency: increase). Motion or induced changes in neuronal activity were excluded as confounders. Results show that rTMS is effective for increasing and decreasing functional coherence between prefrontal and limbic regions. This finding is relevant for social and affective neuroscience as well as novel treatment approaches in psychiatry.

Concentric radiofrequency arrays to increase the statistical power of resting-state maps in monkeys.

The close homology of monkeys and humans has increased the prevalence of non-human-primate models in functional MRI studies of brain connectivity. To improve upon the attainable resolution in functional MRI studies, a commensurate increase in the sensitivity of the radiofrequency receiver coil is required to avoid a reduction in the statistical power of the analysis. Most receive coils are comprised of multiple loops distributed equidistantly over a surface to produce spatially independent sensitivity profiles. A larger number of smaller elements will in turn provide a higher signal-to-noise ratio (SNR) over the same field of view. As the loops become physically smaller, noise originating from the sample is reduced relative to noise originating from the coil. In this coil-noise-dominated regime, coil elements can have overlapping sensitivity profiles, yet still possess only mildly correlated noise. In this manuscript, we demonstrate that inductively decoupled, concentric coil arrays can improve temporal SNR when operating in the coil-noise-dominated regime-in contrast to what is expected for the more ubiquitous sample-noise-dominated array. A small, thin, 7-channel flexible coil is developed and operated in conjunction with an existing whole-head monkey coil. The mean and maximum noise correlation between the two arrays was 5% and 23%, respectively. When the flex coil was placed over the sensorimotor cortex, the temporal SNR improved by up to 2.3-fold in the peripheral cortex and up to 1.3-fold at a 2- to 3-cm depth within the brain. When the flex coil was placed over the frontal eye fields, resting-state maps showed substantially elevated sensitivity to correlations in the prefrontal cortex (54%), supplementary eye fields (39%), and anterior cingulate cortex (41%). The concentric-coil topology provided a pragmatic and robust means to significantly improve local temporal SNR and the statistical power of functional connectivity maps.

Fluctuations of the EEG-fMRI correlation reflect intrinsic strength of functional connectivity in default mode network.

Both functional magnetic resonance imaging (fMRI) and electrophysiological recordings have revealed that resting-state functional connectivity is temporally variable in human brain. Combined full-band electroencephalography-fMRI (fbEEG-fMRI) studies have shown that infraslow (<.1 Hz) fluctuations in EEG scalp potential are correlated with the blood-oxygen-level-dependent (BOLD) fMRI signals and that also this correlation appears variable over time. Here, we used simultaneous fbEEG-fMRI to test the hypothesis that correlation dynamics between BOLD and fbEEG signals could be explained by fluctuations in the activation properties of resting-state networks (RSNs) such as the extent or strength of their activation. We used ultrafast magnetic resonance encephalography (MREG) fMRI to enable temporally accurate and statistically robust short-time-window comparisons of infra-slow fbEEG and BOLD signals. We found that the temporal fluctuations in the fbEEG-BOLD correlation were dependent on RSN connectivity strength, but not on the mean signal level or magnitude of RSN activation or motion during scanning. Moreover, the EEG-fMRI correlations were strongest when the intrinsic RSN connectivity was strong and close to the pial surface. Conversely, weak fbEEG-BOLD correlations were attributable to periods of less coherent or spatially more scattered intrinsic RSN connectivity, or RSN activation in deeper cerebral structures. The results thus show that the on-average low correlations between infra-slow EEG and BOLD signals are, in fact, governed by the momentary coherence and depth of the underlying RSN activation, and may reach systematically high values with appropriate source activities. These findings further consolidate the notion of slow scalp potentials being directly coupled to hemodynamic fluctuations.

Neural Substrates of Auditory Emotion Recognition Deficits in Schizophrenia.

Deficits in auditory emotion recognition (AER) are a core feature of schizophrenia and a key component of social cognitive impairment. AER deficits are tied behaviorally to impaired ability to interpret tonal ("prosodic") features of speech that normally convey emotion, such as modulations in base pitch (F0M) and pitch variability (F0SD). These modulations can be recreated using synthetic frequency modulated (FM) tones that mimic the prosodic contours of specific emotional stimuli. The present study investigates neural mechanisms underlying impaired AER using a combined event-related potential/resting-state functional connectivity (rsfMRI) approach in 84 schizophrenia/schizoaffective disorder patients and 66 healthy comparison subjects. Mismatch negativity (MMN) to FM tones was assessed in 43 patients/36 controls. rsfMRI between auditory cortex and medial temporal (insula) regions was assessed in 55 patients/51 controls. The relationship between AER, MMN to FM tones, and rsfMRI was assessed in the subset who performed all assessments (14 patients, 21 controls). As predicted, patients showed robust reductions in MMN across FM stimulus type (p = 0.005), particularly to modulations in F0M, along with impairments in AER and FM tone discrimination. MMN source analysis indicated dipoles in both auditory cortex and anterior insula, whereas rsfMRI analyses showed reduced auditory-insula connectivity. MMN to FM tones and functional connectivity together accounted for ∼50% of the variance in AER performance across individuals. These findings demonstrate that impaired preattentive processing of tonal information and reduced auditory-insula connectivity are critical determinants of social cognitive dysfunction in schizophrenia, and thus represent key targets for future research and clinical intervention. SIGNIFICANCE STATEMENT: Schizophrenia patients show deficits in the ability to infer emotion based upon tone of voice [auditory emotion recognition (AER)] that drive impairments in social cognition and global functional outcome. This study evaluated neural substrates of impaired AER in schizophrenia using a combined event-related potential/resting-state fMRI approach. Patients showed impaired mismatch negativity response to emotionally relevant frequency modulated tones along with impaired functional connectivity between auditory and medial temporal (anterior insula) cortex. These deficits contributed in parallel to impaired AER and accounted for ∼50% of variance in AER performance. Overall, these findings demonstrate the importance of both auditory-level dysfunction and impaired auditory/insula connectivity in the pathophysiology of social cognitive dysfunction in schizophrenia.

Structural and resting-state MRI detects regional brain differences in young and mid-age healthy APOE-e4 carriers compared with non-APOE-e4 carriers.

The presence of the e4 allele of the apolipoprotein E (APOE) gene is the best-known genetic risk factor for Alzheimer's disease. In this study, we investigated the link between functional and behavioural differences and regional brain volume and cortical thickness differences in those who carry the e4 allele (e4+) and those who only carry the e3 allele (e3/e3). We studied these genotype populations in two age groups: a young group (average age, 21 years) and a mid-age group (average age, 50 years). High-resolution T1 -weighted MRI scans were analysed with Freesurfer to measure regional white matter brain volume and cortical thickness differences between genotype groups at each age. These data were correlated with behavioural findings in the same cohort. Resting-state MRI was also conducted to identify differences in underlying brain functional connectivity. We found that there was a positive correlation between the thickness of the parahippocampal cortex in young e4+ individuals and performance on an episodic memory task. Young e4+ individuals also showed a positive correlation between white matter volume in the left anterior cingulate and performance on a covert attention task. At mid-age, e4+ individuals had structural differences relative to e3/e3 individuals in these areas: the parahippocampal cortex was thicker and white matter volume in the left anterior cingulate was greater than in e3/e3 individuals. We discuss the possibility that an over-engagement with these regions by e4+ individuals in youth may have a neurogenic effect that is observable later in life. The cuneus appears to be an important region for APOE-driven differences in the brain, with greater functional connectivity among young e3/e3 individuals and greater white matter volume in young e4+ individuals.

Interhemispheric Functional Brain Connectivity in Neonates with Prenatal Alcohol Exposure: Preliminary Findings.

BACKGROUND: Children exposed to alcohol in utero demonstrate reduced white matter microstructural integrity. While early evidence suggests altered functional brain connectivity in the lateralization of motor networks in school-age children with prenatal alcohol exposure (PAE), the specific effects of alcohol exposure on the establishment of intrinsic connectivity in early infancy have not been explored. METHODS: Sixty subjects received functional imaging at 2 to 4 weeks of age for 6 to 8 minutes during quiet natural sleep. Thirteen alcohol-exposed (PAE) and 14 age-matched control (CTRL) participants with usable data were included in a multivariate model of connectivity between sensorimotor intrinsic functional connectivity networks. Seed-based analyses of group differences in interhemispheric connectivity of intrinsic motor networks were also conducted. The Dubowitz neurological assessment was performed at the imaging visit. RESULTS: Alcohol exposure was associated with significant increases in connectivity between somatosensory, motor networks, brainstem/thalamic, and striatal intrinsic networks. Reductions in interhemispheric connectivity of motor and somatosensory networks did not reach significance. CONCLUSIONS: Although results are preliminary, findings suggest PAE may disrupt the temporal coherence in blood oxygenation utilization in intrinsic networks underlying motor performance in newborn infants. Studies that employ longitudinal designs to investigate the effects of in utero alcohol exposure on the evolving resting-state networks will be key in establishing the distribution and timing of connectivity disturbances already described in older children.

Multicontrast connectometry: a new tool to assess cerebellum alterations in early relapsing-remitting multiple sclerosis.

BACKGROUND: Cerebellar pathology occurs in late multiple sclerosis (MS) but little is known about cerebellar changes during early disease stages. In this study, we propose a new multicontrast "connectometry" approach to assess the structural and functional integrity of cerebellar networks and connectivity in early MS. METHODS: We used diffusion spectrum and resting-state functional MRI (rs-fMRI) to establish the structural and functional cerebellar connectomes in 28 early relapsing-remitting MS patients and 16 healthy controls (HC). We performed multicontrast "connectometry" by quantifying multiple MRI parameters along the structural tracts (generalized fractional anisotropy-GFA, T1/T2 relaxation times and magnetization transfer ratio) and functional connectivity measures. Subsequently, we assessed multivariate differences in local connections and network properties between MS and HC subjects; finally, we correlated detected alterations with lesion load, disease duration, and clinical scores. RESULTS: In MS patients, a subset of structural connections showed quantitative MRI changes suggesting loss of axonal microstructure and integrity (increased T1 and decreased GFA, P < 0.05). These alterations highly correlated with motor, memory and attention in patients, but were independent of cerebellar lesion load and disease duration. Neither network organization nor rs-fMRI abnormalities were observed at this early stage. CONCLUSION: Multicontrast cerebellar connectometry revealed subtle cerebellar alterations in MS patients, which were independent of conventional disease markers and highly correlated with patient function. Future work should assess the prognostic value of the observed damage.

Apolipoprotein E genotype, gender and age modulate connectivity of the hippocampus in healthy adults.

Important risk factors for Alzheimer's disease (AD) are ageing and the Apolipoprotein E (APOE) ε4 allele, with female APOE ε4 carriers having the greatest risk. In this study we investigated effects of AD risk factors on connectivity of the hippocampus, a structure that shows early AD related pathology. Resting-state functional magnetic resonance imaging and diffusion tensor imaging data from 86 cognitively healthy subjects aged 30 to 78years were analysed. Female APOE ε4 carriers showed overall significantly reduced functional connectivity between the hippocampus and precuneus/posterior cingulate cortex (PCC) and a significant age-related decrease in connectivity of these regions. In females and APOE ε4 carriers we found significantly reduced white matter integrity of the tract connecting the hippocampus and PCC with a significant positive correlation of white matter integrity and resting-state connectivity. Increased vulnerability of the connection between the hippocampus and PCC might be one reason for increased AD risk in female APOE ε4 carriers. Interventions targeting hippocampal connectivity might be especially effective in this at risk population.

The long-term impact of irradiation on functional connectivity in brain circuits involved in memory processes after pediatric posterior fossa tumor.

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.

Active suicidal ideation associated with dysfunction in default mode network using resting-state EEG and functional MRI - Findings from the T-RAD Study.

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.

Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI.

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.