Altered resting-state activity in seasonal affective disorder

Effects of seasonality in emergency admissions for mental disorders: two years of clinical experience

OBJECTIVES

This retrospective study, conducted in Turin, Italy, between January 2021 and February 2023, investigates the impact of seasonal heatwaves on emergency department (ED) admissions for mental disorders.

METHODS

Through the analysis of data from 2,854 patients, this research found a significant link between the occurrence of heatwaves, especially from June to August, and an elevated rate of ED admissions for psychiatric conditions.

RESULTS

The data indicate a clear seasonal pattern, with admissions peaking during the hot months and diminishing in the colder months. Particularly, the study delineates an enhanced correlation between heatwaves and admissions for severe psychiatric disorders, such as bipolar disorder, major depression, personality disorders, and schizophrenia, accounting for 1,868 of the cases examined. This correlation was most pronounced among individuals aged 50-59 years.

CONCLUSIONS

The results of this study highlight a critical association between the incidence of seasonal heatwaves and an uptick in ED visits for psychiatric disorders, with a distinct impact on severe cases. It underscores the urgency for healthcare systems to anticipate seasonal fluctuations in psychiatric ED admissions and to allocate resources effectively to support patients during peak periods.

Neural signatures of shared subjective affective engagement and disengagement during movie viewing

When watching a negative emotional movie, we differ from person to person in the ease with which we engage and the difficulty with which we disengage throughout a temporally evolving narrative. We investigated neural responses of emotional processing, by considering inter-individual synchronization in subjective emotional engagement and disengagement. The neural underpinnings of these shared responses are ideally studied in naturalistic scenarios like movie viewing, wherein individuals emotionally engage and disengage at their own time and pace throughout the course of a narrative. Despite the rich data that naturalistic designs can bring to the study, there is a challenge in determining time-resolved behavioral markers of subjective engagement and disengagement and their underlying neural responses. We used a within-subject cross-over design instructing 22 subjects to watch clips of either neutral or sad content while undergoing functional magnetic resonance imaging (fMRI). Participants watched the same movies a second time while continuously annotating the perceived emotional intensity, thus enabling the mapping of brain activity and emotional experience. Our analyses revealed that between-participant similarity in waxing (engagement) and waning (disengagement) of emotional intensity was directly related to the between-participant similarity in spatiotemporal patterns of brain activation during the movie(s). Similar patterns of engagement reflected common activation in the bilateral ventromedial prefrontal cortex, regions often involved in self-referenced evaluation and generation of negative emotions. Similar patterns of disengagement reflected common activation in central executive and default mode network regions often involved in top-down emotion regulation. Together this work helps to better understand cognitive and neural mechanisms underpinning engagement and disengagement from emotionally evocative narratives.

Seasonal variations of functional connectivity of human brains

Seasonal variations have long been observed in various aspects of human life. While there is an abundance of research that has characterized seasonality effects in, for example, cognition, mood, and behavior, including studies of underlying biophysical mechanisms, direct measurements of seasonal variations of brain functional activities have not gained wide attention. We have quantified seasonal effects on functional connectivity as derived from MRI scans. A cohort of healthy human subjects was divided into four groups based on the seasons of their scanning dates as documented in the image database of the Human Connectome Project. Sinusoidal functions were used as regressors to determine whether there were significant seasonal variations in measures of brain activities. We began with the analysis of seasonal variations of the fractional amplitudes of low frequency fluctuations of regional functional signals, followed by the seasonal variations of functional connectivity in both global- and network-level. Furthermore, relevant environmental factors, including average temperature and daylength, were found to be significantly associated with brain functional activities, which may explain how the observed seasonal fluctuations arise. Finally, topological properties of the brain functional network also showed significant variations across seasons. All the observations accumulated revealed seasonality effects of human brain activities in a resting-state, which may have important practical implications for neuroimaging research.

Resting-state functional-MRI in iNPH: can default mode and motor networks changes improve patient selection and outcome? Preliminary report

BACKGROUND

Idiopathic normal pressure hydrocephalus (iNPH) is a progressive and partially reversible form of dementia, characterized by impaired interactions between multiple brain regions. Because of the presence of comorbidities and a lack of accurate diagnostic and prognostic biomarkers, only a minority of patients receives disease-specific treatment. Recently, resting-state functional-magnetic resonance imaging (rs-fMRI) has demonstrated functional connectivity alterations in inter-hemispheric, frontal, occipital, default-mode (DMN) and motor network (MN) circuits. Herein, we report our experience in a cohort of iNPH patients that underwent cerebrospinal fluid (CSF) dynamics evaluation and rs-fMRI. The study aimed to identify functional circuits related to iNPH and explore the relationship between DMN and MN recordings and clinical modifications before and after infusion and tap test, trying to understand iNPH pathophysiology and to predict the best responders to ventriculoperitoneal shunt (VPS) implant.

METHODS

We prospectively collected data regarding clinical assessment, neuroradiological findings, lumbar infusion and tap test of thirty-two iNPH patients who underwent VPS implant. Rs-fMRI was performed using MELODIC-ICA both before and after the tap test. Rs-fMRI data of thirty healthy subjects were also recorded.

RESULTS

At the baseline, reduced z-DMN and z-MN scores were recorded in the iNPH cohort compared with controls. Higher z-scores were recorded in more impaired patients. Both z-scores significantly improved after the tap test except in subjects with a low resistance to outflow value and without a significant clinical improvement after the test. A statistically significant difference in mean MN connectivity scores for tap test responders and non-responders was demonstrated both before (p = 0.0236) and after the test (p = 0.00137). A statistically significant main effect of the tap test on DMN connectivity after CSF subtraction was recorded (p = 0.038).

CONCLUSIONS

Our results suggest the presence of a partially reversible plasticity functional mechanism in DMN and MN. Low values compensate for the initial stages of the disease, while higher values of z-DMN were recorded in older patients with a longer duration of symptoms, suggesting an exhausted plasticity compensation. The standardization of this technique could play a role as a non-invasive biomarker in iNPH disease, suggesting the right time for surgery. Trial Registration Prot. IRB 090/2021.

Estimations of the weather effects on brain functions using functional MRI: A cautionary note

The influences of environmental factors such as weather on the human brain are still largely unknown. A few neuroimaging studies have demonstrated seasonal effects, but were limited by their cross‐sectional design or sample sizes. Most importantly, the stability of the MRI scanner has not been taken into account, which may also be affected by environments. In the current study, we analyzed longitudinal resting‐state functional MRI (fMRI) data from eight individuals, where they were scanned over months to years. We applied machine learning regression to use different resting‐state parameters, including the amplitude of low‐frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity matrix, to predict different weather and environmental parameters. For careful control, the raw EPI and the anatomical images were also used for predictions. We first found that daylight length and air temperatures could be reliably predicted with cross‐validation using the resting‐state parameters. However, similar prediction accuracies could also be achieved by using one frame of EPI image, and even higher accuracies could be achieved by using the segmented or raw anatomical images. Finally, the signals outside of the brain in the anatomical images and signals in phantom scans could also achieve higher prediction accuracies, suggesting that the predictability may be due to the baseline signals of the MRI scanner. After all, we did not identify detectable influences of weather on brain functions other than the influences on the baseline signals of MRI scanners. The results highlight the difficulty of studying long‐term effects using MRI.

Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia

The human brain is a dynamic system that incorporates the evolution of local activities and the reconfiguration of brain interactions. Reoccurring brain patterns, regarded as "brain states", have revealed new insights into the pathophysiology of brain disorders, particularly schizophrenia. However, previous studies only focus on the dynamics of either brain activity or connectivity, ignoring the temporal co-evolution between them. In this work, we propose to capture dynamic brain states with covarying activity-connectivity and probe schizophrenia-related brain abnormalities. We find that the state-based activity and connectivity show high correspondence, where strong and antagonistic connectivity is accompanied with strong low-frequency fluctuations across the whole brain while weak and sparse connectivity co-occurs with weak low-frequency fluctuations. In addition, graphical analysis shows that connectivity network efficiency is associated with the fluctuation of brain activities and such associations are different across brain states. Compared with healthy controls, schizophrenia patients spend more time in weakly-connected and -activated brain states but less time in strongly-connected and -activated brain states. schizophrenia patients also show lower efficiency in thalamic regions within the "strong" states. Interestingly, the atypical fractional occupancy of one brain state is correlated with individual attention performance. Our findings are replicated in another independent dataset and validated using different brain parcellation schemes. These converging results suggest that the brain spontaneously reconfigures with covarying activity and connectivity and such co-evolutionary property might provide meaningful information on the mechanism of brain disorders which cannot be observed by investigating either of them alone.

KIBRA and APOE Gene Variants Affect Brain Functional Network Connectivity in Healthy Older People

Genetic factors play a critical role in the development of Alzheimer’s disease (AD). Kidney and brain expressed protein (KIBRA) and apolipoprotein E (APOE) are involved in episodic memory performance and AD. However, the interactions between KIBRA and APOE on brain functional network connectivity (FNC) remain unknown in healthy older people. Using independent component analysis, we systematically investigated additive and epistatic interactions of KIBRA rs1707045 and APOE on FNC in 170 healthy older Chinese people of Han ethnicity. We found significant additive KIBRA–APOE interactions on brain FNC in the right medial prefrontal cortex, the posterior cingulate cortex in the default-mode network, and the dorsal anterior cingulate cortex in the salience network. We also found significant epistatic KIBRA–APOE interactions on brain FNC in the left superior frontal gyrus and left angular gyrus in default-mode network. No significant KIBRA–APOE interactions were detected in other brain resting-state networks. These findings suggest that healthy older people have additive and epistatic interactions of KIBRA and APOE gene variants, which modulate brain FNC and may partly elucidate their association with episodic memory performance and AD.

Amygdala response to emotional faces in seasonal affective disorder

BACKGROUND

Seasonal affective disorder (SAD) is characterized by seasonally recurring depression. Heightened amygdala activation to aversive stimuli is associated with major depressive disorder but its relation to SAD is unclear. We evaluated seasonal variation in amygdala activation in SAD and healthy controls (HC) using a longitudinal design targeting the asymptomatic/symptomatic phases of SAD. We hypothesized increased amygdala activation to aversive stimuli in the winter in SAD individuals (season-by-group interaction).

METHODS

Seventeen SAD individuals and 15 HCs completed an implicit emotional faces BOLD-fMRI paradigm during summer and winter. We computed amygdala activation (SPM5) to an aversive contrast (angry & fearful minus neutral) and angry, fearful and neutral faces, separately. Season-by-group and main effects were evaluated using Generalized Least Squares. In SAD individuals, we correlated change in symptom severity, assessed with The Hamilton Rating Scale for Depression - Seasonal Affective Disorder version (SIGH-SAD), with change in amygdala activation.

RESULTS

We found no season-by-group, season or group effect on our aversive contrast. Independent of season, SAD individuals showed significantly lower amygdala activation to all faces compared to healthy controls, with no evidence for a season-by-group interaction. Seasonal change in amygdala activation was unrelated to change in SIGH-SAD.

LIMITATIONS

Small sample size, lack of positive valence stimuli.

CONCLUSIONS

Amygdala activation to aversive faces is not increased in symptomatic SAD individuals. Instead, we observed decreased amygdala activation across faces, independent of season. Our findings suggest that amygdala activation to angry, fearful and neutral faces is altered in SAD individuals, independent of the presence of depressive symptoms.

Synchronization and variability imbalance underlie cognitive impairment in primary-progressive multiple sclerosis

We aimed to investigate functional connectivity and variability across multiple frequency bands in brain networks underlying cognitive deficits in primary-progressive multiple sclerosis (PP-MS) and to explore how they are affected by the presence of cortical lesions (CLs). We analyzed functional connectivity and variability (measured as the standard deviation of BOLD signal amplitude) in resting state networks (RSNs) associated with cognitive deficits in different frequency bands in 25 PP-MS patients (12 M, mean age 50.9 ± 10.5 years) and 20 healthy subjects (9 M, mean age 51.0 ± 9.8 years). We confirmed the presence of a widespread cognitive deterioration in PP-MS patients, with main involvement of visuo-spatial and executive domains. Cognitively impaired patients showed increased variability, reduced synchronicity between networks involved in the control of cognitive macro-domains and hyper-synchronicity limited to the connections between networks functionally more segregated. CL volume was higher in patients with cognitive impairment and was correlated with functional connectivity and variability. We demonstrate, for the first time, that a functional reorganization characterized by hypo-synchronicity of functionally-related/hyper-synchronicity of functionally-segregated large scale networks and an abnormal pattern of neural activity underlie cognitive dysfunction in PP-MS, and that CLs possibly play a role in variability and functional connectivity abnormalities.

The distribution of melanopsin (OPN4) protein in the human brain

Until now, melanopsin (OPN4) - a specialized photopigment being responsive especially to blue light wavelengths - has not been found in the human brain at protein level outside the retina. More specifically, OPN4 has only been found in about 2% of retinal ganglion cells (i.e. in intrinsically photosensitive retinal ganglion cells), and in a subtype of retinal cone-cells. Given that Allen Institute for Brain Science has described a wide distribution of OPN4 mRNA in two human brains, we aimed to investigate whether OPN4 is present in the human brain also at protein level. Western blotting and immunohistochemistry, as well as immunoelectron microscopy, were used to analyse the existence and distribution of OPN4 protein in 18 investigated areas of the human brain in samples obtained in forensic autopsies from 10 male subjects (54 ± 3.5 years). OPN4 protein expression was found in all subjects, and, furthermore, in 5 out of 10 subjects in all investigated brain areas localized in membranous compartments and cytoplasmic vesicles of neurons. To our opinion, the wide distribution of OPN4 in central areas of the human brain evokes a question whether ambient light has important straight targets in the human brain outside the retinohypothalamic tract (RHT). Further studies are, however, needed to investigate the putative physiological phototransductive actions of inborn OPN4 protein outside the RHT in the human brain.

Abnormal resting-state functional connectivity of the left caudate nucleus in obsessive-compulsive disorder

Altered brain activities in the cortico-striato-thalamocortical (CSTC) circuitry are implicated in the pathophysiology of obsessive-compulsive disorder (OCD). However, whether the underlying changes occur only within this circuitry or in large-scale networks is still not thoroughly understood. This study performed voxel-based functional connectivity analysis on resting-state functional magnetic resonance imaging (fMRI) data from thirty OCD patients and thirty healthy controls to investigate whole-brain intrinsic functional connectivity patterns in OCD. Relative to the healthy controls, OCD patients showed decreased functional connectivity within the CSTC circuitry but increased functional connectivity in other brain regions. Furthermore, decreased left caudate nucleus-thalamus connectivity within the CSTC circuitry was positively correlated with the illness duration of OCD. This study provides additional evidence that CSTC circuitry may play an essential role and alteration of large-scale brain networks may be involved in the pathophysiology of OCD.

Non-parametric combination and related permutation tests for neuroimaging

In this work, we show how permutation methods can be applied to combination analyses such as those that include multiple imaging modalities, multiple data acquisitions of the same modality, or simply multiple hypotheses on the same data. Using the well-known definition of union-intersection tests and closed testing procedures, we use synchronized permutations to correct for such multiplicity of tests, allowing flexibility to integrate imaging data with different spatial resolutions, surface and/or volume-based representations of the brain, including non-imaging data. For the problem of joint inference, we propose and evaluate a modification of the recently introduced non-parametric combination (NPC) methodology, such that instead of a two-phase algorithm and large data storage requirements, the inference can be performed in a single phase, with reasonable computational demands. The method compares favorably to classical multivariate tests (such as MANCOVA), even when the latter is assessed using permutations. We also evaluate, in the context of permutation tests, various combining methods that have been proposed in the past decades, and identify those that provide the best control over error rate and power across a range of situations. We show that one of these, the method of Tippett, provides a link between correction for the multiplicity of tests and their combination. Finally, we discuss how the correction can solve certain problems of multiple comparisons in one-way ANOVA designs, and how the combination is distinguished from conjunctions, even though both can be assessed using permutation tests. We also provide a common algorithm that accommodates combination and correction.

Spatiotemporal psychopathology I: No rest for the brain's resting state activity in depression? Spatiotemporal psychopathology of depressive symptoms

Despite intense neurobiological investigation in psychiatric disorders like major depressive disorder (MDD), the basic disturbance that underlies the psychopathological symptoms of MDD remains, nevertheless, unclear. Neuroimaging has focused mainly on the brain's extrinsic activity, specifically task-evoked or stimulus-induced activity, as related to the various sensorimotor, affective, cognitive, and social functions. Recently, the focus has shifted to the brain's intrinsic activity, otherwise known as its resting state activity. While various abnormalities have been observed during this activity, their meaning and significance for depression, along with its various psychopathological symptoms, are yet to be defined. Based on findings in healthy brain resting state activity and its particular spatial and temporal structure - defined in a functional and physiological sense rather than anatomical and structural - I claim that the various depressive symptoms are spatiotemporal disturbances of the resting state activity and its spatiotemporal structure. This is supported by recent findings that link ruminations and increased self-focus in depression to abnormal spatial organization of resting state activity. Analogously, affective and cognitive symptoms like anhedonia, suicidal ideation, and thought disorder can be traced to an increased focus on the past, increased past-focus as basic temporal disturbance o the resting state. Based on these findings, I conclude that the various depressive symptoms must be conceived as spatiotemporal disturbances of the brain's resting state's activity and its spatiotemporal structure. Importantly, this entails a new form of psychopathology, "Spatiotemporal Psychopathology" that directly links the brain and psyche, therefore having major diagnostic and therapeutic implications for clinical practice.

Decoupled temporal variability and signal synchronization of spontaneous brain activity in loss of consciousness: An fMRI study in anesthesia

Two aspects of the low frequency fluctuations of spontaneous brain activity have been proposed which reflect the complex and dynamic features of resting-state activity, namely temporal variability and signal synchronization. The relationship between them, especially its role in consciousness, nevertheless remains unclear. Our study examined the temporal variability and signal synchronization of spontaneous brain activity, as well as their relationship during loss of consciousness. We applied an intra-subject design of resting-state functional magnetic resonance imaging (rs-fMRI) in two conditions: during wakefulness, and under anesthesia with clinical unconsciousness. In addition, an independent group of patients with disorders of consciousness (DOC) was included in order to test the reliability of our findings. We observed a global reduction in the temporal variability, local and distant brain signal synchronization for subjects during anesthesia. Importantly, we found a link between temporal variability and both local and distant signal synchronizations during wakefulness: the higher the degree of temporal variability, the higher its intra-regional homogeneity and inter-regional functional connectivity. In contrast, this link was broken down under anesthesia, implying a decoupling between temporal variability and signal synchronization; this decoupling was reproduced in patients with DOC. Our results suggest that there exist some as yet unclear physiological mechanisms of consciousness which "couple" the two mathematically independent measures, temporal variability and signal synchronization of spontaneous brain activity. Our findings not only extend our current knowledge of the neural correlates of anesthetic-induced unconsciousness, but have implications for both computational neural modeling and clinical practice, such as in the diagnosis of loss of consciousness in patients with DOC.

Altered regional activity and inter-regional functional connectivity in psychogenic non-epileptic seizures

Although various imaging studies have focused on detecting the cerebral function underlying psychogenic non-epileptic seizures (PNES), the nature of PNES remains poorly understood. In this study, we combined the resting state fMRI with fractional amplitude of low-frequency fluctuations (fALFF) and functional connectivity based on the seed voxel linear correlation approach to examine the alterations of regional and inter-regional network cerebral functions in PNES. A total of 20 healthy controls and 18 patients were enrolled. The PNES patients showed significantly increased fALFF mainly in the dorsolateral prefrontal cortex (DLPFC), parietal cortices, and motor areas, as well as decreased fALFF in the triangular inferior frontal gyrus. Thus, our results add to literature suggesting abnormalities of neural synchrony in PNES. Moreover, PNES exhibited widespread inter-regional neural network deficits, including increased (DLPFC, sensorimotor, and limbic system) and decreased (ventrolateral prefrontal cortex) connectivity, indicating that changes in the regional cerebral function are related to remote inter-regional network deficits. Correlation analysis results revealed that the connectivity between supplementary motor area and anterior cingulate cortex correlated with the PNES frequency, further suggesting the skewed integration of synchronous activity could predispose to the occurrence of PNES. Our findings provided novel evidence to investigate the pathophysiological mechanisms of PNES.

Frequency-dependent changes of local resting oscillations in sleep-deprived brain

Sleep deprivation (SD) adversely affects brain function and is accompanied by frequency dependent changes in EEG. Recent studies have suggested that BOLD fluctuations pertain to a spatiotemporal organization with different frequencies. The present study aimed to investigate the frequency-dependent SD-related brain oscillatory activity by using the amplitude of low-frequency fluctuation (ALFF) analysis. The ALFF changes were measured across different frequencies (Slow-4: 0.027–0.073 Hz; Slow-5: 0.01–0.027 Hz; and Typical band: 0.01–0.08 Hz) in 24 h SD as compared to rested wakeful during resting-state fMRI. Sixteen volunteers underwent two fMRI sessions, once during rested wakefulness and once after 24 h of SD. SD showed prominently decreased ALFF in the right inferior parietal lobule (IPL), bilateral orbitofrontal cortex (OFC) and dorsolateral prefrontal cortex (DLPFC), while increased ALFF in the visual cortex, left sensorimotor cortex and fusiform gyrus. Across the Slow-4 and Slow-5, results differed significantly in the OFC, DLPFC, thalamus and caudate in comparison to typical frequency band; and Slow-4 showed greater differences. In addition, negative correlations of behavior performance and ALFF patterns were found mainly in the right IPL across the typical frequency band. These observations provided novel insights about the physiological responses of SD, identified how it disturbs the brain rhythms, and linked SD with frequency-dependent alterations in amplitude patterns.

Graph theory reveals hyper-functionality in visual cortices of Seasonal Affective Disorder patients

OBJECTIVES

Seasonal affective disorder (SAD) is a subtype of recurrent unipolar or bipolar depressive disorder with a higher prevalence in winter than in summer. The biological underpinnings of SAD are so far poorly understood. Studies examining SAD have found disturbances between the molecular and connectivity scales. The aim of the study was to explore changes in functional connectivity typical for SAD.

METHODS

We investigated unmedicated, untreated SAD patients and healthy controls using resting-state functional magnetic resonance imaging (rs-fMRI) utilizing graph theory, a data driven and hypothesis free approach, to model functional networks of the brain.

RESULTS

Comparing whole brain network properties using graph theory we observed globally affected network topologies with increasing pathlength in SAD. Nodal changes, however, were highly restricted to bilateral inferior occipital cortex. Interestingly, we found a lateralization where hyper-connectedness was restricted to right inferior occipital cortex and hyper-efficiency was found in the left inferior occipital cortex. Furthermore, we found these nodes became more "hub like" in patients, suggesting a greater functional role.

CONCLUSIONS

Our work stresses the importance of abnormal intrinsic processing during rest, primarily affecting visual areas and subsequently changing whole brain networks, and thus providing an important hint towards potential future therapeutic approaches.

Aberrant Functional Connectivity in the Default Mode and Central Executive Networks in Subjects with Schizophrenia - A Whole-Brain Resting-State ICA Study

Neurophysiological changes of schizophrenia are currently linked to disturbances in connectivity between functional brain networks. Functional magnetic resonance imaging studies on schizophrenia have focused on a few selected networks. Also previously, it has not been possible to discern whether the functional alterations in schizophrenia originate from spatial shifting or amplitude alterations of functional connectivity. In this study, we aim to discern the differences in schizophrenia patients with respect to spatial shifting vs. signal amplitude changes in functional connectivity in the whole-brain connectome. We used high model order-independent component analysis to study some 40 resting-state networks (RSN) covering the whole cortex. Group differences were analyzed with dual regression coupled with y-concat correction for multiple comparisons. We investigated the RSNs with and without variance normalization in order to discern spatial shifting from signal amplitude changes in 43 schizophrenia patients and matched controls from the Northern Finland 1966 Birth Cohort. Voxel-level correction for multiple comparisons revealed 18 RSNs with altered functional connectivity, 6 of which had both spatial and signal amplitude changes. After adding the multiple comparison, y-concat correction to the analysis for including the 40 RSNs as well, we found that four RSNs showed still changes. These robust changes actually seem encompass parcellations of the default mode network and central executive networks. These networks both have spatially shifted connectivity and abnormal signal amplitudes. Interestingly the networks seem to mix their functional representations in areas like left caudate nucleus and dorsolateral prefrontal cortex. These changes overlapped with areas that have been related to dopaminergic alterations in patients with schizophrenia compared to controls.

Resting-state functional connectivity in women with Major Depressive Disorder

OBJECTIVE

Limited research has focused on whole-brain functional connectivity in a well-characterized sample of subjects with current Major Depressive Disorder (MDD). We aimed to investigate resting-state functional connectivity and the extent to which this is correlated with depression severity in unmedicated depressed subjects without comorbidities.

METHODS

We utilized Independent Component Analysis (ICA) to investigate whole-brain functional connectivity in a sample of healthy controls (n = 26) and unmedicated subjects diagnosed only with current MDD (n = 20). Correlations were calculated between network connectivity strength and depression severity.

RESULTS

Depressed subjects demonstrated significantly decreased connectivity in the right frontoparietal (p = 0.03), left frontoparietal (p = 0.01), and language (p = 0.02) networks compared to healthy control subjects.

CONCLUSION

We found abnormal resting-state functional connectivity not previously reported in MDD. Decreased connectivity in the frontoparietal and language networks may represent depression-related difficulties in attention, cognitive control, goal-directed cognition, and language. Findings from this study may further elucidate functional connectivity as a diagnostic marker of depression severity.

Synchronous multiscale neuroimaging environment for critically sampled physiological analysis of brain function: hepta-scan concept

Functional connectivity of the resting-state networks of the brain is thought to be mediated by very-low-frequency fluctuations (VLFFs <0.1 Hz) in neuronal activity. However, vasomotor waves and cardiorespiratory pulsations influence indirect measures of brain function, such as the functional magnetic resonance imaging blood-oxygen-level-dependent (BOLD) signal. How strongly physiological oscillations correlate with spontaneous BOLD signals is not known, partially due to differences in the data-sampling rates of different methods. Recent ultrafast inverse imaging sequences, including magnetic resonance encephalography (MREG), enable critical sampling of these signals. In this study, we describe a multimodal concept, referred to as Hepta-scan, which incorporates synchronous MREG with scalp electroencephalography, near-infrared spectroscopy, noninvasive blood pressure, and anesthesia monitoring. Our preliminary results support the idea that, in the absence of aliased cardiorespiratory signals, VLFFs in the BOLD signal are affected by vasomotor and electrophysiological sources. Further, MREG signals showed a high correlation coefficient between the ventromedial default mode network (DMNvmpf) and electrophysiological signals, especially in the VLF range. Also, oxy- and deoxyhemoglobin and vasomotor waves were found to correlate with DMNvmpf. Intriguingly, usage of shorter time windows in these correlation measurements produced significantly (p<0.05) higher positive and negative correlation coefficients, suggesting temporal nonstationary behavior between the measurements. Focus on the VLF range strongly increased correlation strength.

Differences and the relationship in default mode network intrinsic activity and functional connectivity in mild Alzheimer's disease and amnestic mild cognitive impairment

There is evidence that the default mode network (DMN) functional connectivity is impaired in Alzheimer's disease (AD) and few studies also reported a decrease in DMN intrinsic activity, measured by the amplitude of low-frequency fluctuations (ALFFs). In this study, we analyzed the relationship between DMN intrinsic activity and functional connectivity, as well as their possible implications on cognition in patients with mild AD and amnestic mild cognitive impairment (aMCI) and healthy controls. In addition, we evaluated the differences both in connectivity and ALFF values between these groups. We recruited 29 controls, 20 aMCI, and 32 mild AD patients. To identify the DMN, functional connectivity was calculated by placing a seed in the posterior cingulate cortex (PCC). Within the DMN mask obtained, we calculated regional average ALFFs. Compared with controls, aMCI patients showed decreased ALFFs in the temporal region; compared with AD, aMCI showed higher values in the PCC but lower in the temporal area. The mild AD group had lower ALFFs in the PCC compared with controls. There was no difference between the connectivity in the aMCI group compared with the other groups, but AD patients showed decreased connectivity in the frontal, parietal, and PCC. Also, PCC ALFFs correlated to functional connectivity in nearly all subregions. Cognitive tests correlated to connectivity values but not to ALFFs. In conclusion, we found that DMN connectivity and ALFFs are correlated in these groups. Decreased PCC ALFFs disrupt the DMN functional organization, leading to cognitive problems in the AD spectrum.

Altered temporal variance and neural synchronization of spontaneous brain activity in anesthesia

Recent studies at the cellular and regional levels have pointed out the multifaceted importance of neural synchronization and temporal variance of neural activity. For example, neural synchronization and temporal variance has been shown by us to be altered in patients in the vegetative state (VS). This finding nonetheless leaves open the question of whether these abnormalities are specific to VS or rather more generally related to the absence of consciousness. The aim of our study was to investigate the changes of inter- and intra-regional neural synchronization and temporal variance of resting state activity in anesthetic-induced unconsciousness state. Applying an intra-subject design, we compared resting state activity in functional magnetic resonance imaging (fMRI) between awake versus anesthetized states in the same subjects. Replicating previous studies, we observed reduced functional connectivity within the default mode network (DMN) and thalamocortical network in the anesthetized state. Importantly, intra-regional synchronization as measured by regional homogeneity (ReHo) and temporal variance as measured by standard deviation (SD) of the BOLD signal were significantly reduced in especially the cortical midline regions, while increased in the lateral cortical areas in the anesthetized state. We further found significant frequency-dependent effects of SD in the thalamus, which showed abnormally high SD in Slow-5 (0.01-0.027 Hz) in the anesthetized state. Our results show for the first time of altered temporal variance of resting state activity in anesthesia. Combined with our findings in the vegetative state, these findings suggest a close relationship between temporal variance, neural synchronization and consciousness.

Effects of bright light treatment on psychomotor speed in athletes

Purpose: A recent study suggests that transcranial brain targeted light treatment via ear canals may have physiological effects on brain function studied by functional magnetic resonance imaging (fMRI) techniques in humans. We tested the hypothesis that bright light treatment could improve psychomotor speed in professional ice hockey players.

Methods: Psychomotor speed tests with audio and visual warning signals were administered to a Finnish National Ice Hockey League team before and after 24 days of transcranial bright light or sham treatment. The treatments were given during seasonal darkness in the Oulu region (latitude 65 degrees north) when the strain on the players was also very high (10 matches during 24 days). A daily 12-min dose of bright light or sham (n = 11 for both) treatment was given every morning between 8 and 12 am at home with a transcranial bright light device. Mean reaction time and motor time were analyzed separately for both psychomotor tests. Analysis of variance for repeated measures adjusted for age was performed.

Results: Time × group interaction for motor time with a visual warning signal was p = 0.024 after adjustment for age. In Bonferroni post-hoc analysis, motor time with a visual warning signal decreased in the bright light treatment group from 127 ± 43 to 94 ± 26 ms (p = 0.024) but did not change significantly in the sham group 121 ± 23 vs. 110 ± 32 ms (p = 0.308). Reaction time with a visual signal did not change in either group. Reaction or motor time with an audio warning signal did not change in either the treatment or sham group.

Conclusion: Psychomotor speed, particularly motor time with a visual warning signal, improves after transcranial bright light treatment in professional ice-hockey players during the competition season in the dark time of the year.

Resting state FMRI research in child psychiatric disorders

Concurring with the shift from linking functions to specific brain areas towards studying network integration, resting state FMRI (R-FMRI) has become an important tool for delineating the functional network architecture of the brain. Fueled by straightforward data collection, R-FMRI analysis methods as well as studies reporting on R-FMRI have flourished, and already impact research on child- and adolescent psychiatric disorders. Here, we review R-FMRI analysis techniques and outline current methodological debates. Furthermore, we provide an overview of the main R-FMRI findings related to child- and adolescent psychiatric disorders. R-FMRI research has contributed significantly to our understanding of brain function in child and adolescent psychiatry: existing hypotheses based on task-based FMRI were confirmed and new insights into the brain's functional architecture of disorders were established. However, results were not always consistent. While resting state networks are robust and reproducible, neuroimaging research in psychiatric disorders is especially complicated by tremendous phenotypic heterogeneity. It is imperative that we overcome this heterogeneity when integrating neuroimaging into the diagnostic and treatment process. As R-FMRI allows investigating the richness of the human functional connectome and can be easily collected and aggregated into large-scale datasets, it is clear that R-FMRI can be a powerful tool in our quest to understand psychiatric pathology.

Functional connectivity in the developing brain: a longitudinal study from 4 to 9months of age

We characterize the development of intrinsic connectivity networks (ICNs) from 4 to 9months of age with resting state magnetic resonance imaging performed on sleeping infants without sedative medication. Data is analyzed with independent component analysis (ICA). Using both low (30 components) and high (100 components) ICA model order decompositions, we find that the functional network connectivity (FNC) map is largely similar at both 4 and 9months. However at 9months the connectivity strength decreases within local networks and increases between more distant networks. The connectivity within the default-mode network, which contains both local and more distant nodes, also increases in strength with age. The low frequency power spectrum increases with age only in the posterior cingulate cortex and posterior default mode network. These findings are consistent with a general developmental pattern of increasing longer distance functional connectivity over the first year of life and raise questions regarding the developmental importance of the posterior cingulate at this age.

GroupICA dual regression analysis of resting state networks in a behavioral variant of frontotemporal dementia

Functional MRI studies have revealed changes in default-mode and salience networks in neurodegenerative dementias, especially in Alzheimer's disease (AD). The purpose of this study was to analyze the whole brain cortex resting state networks (RSNs) in patients with behavioral variant frontotemporal dementia (bvFTD) by using resting state functional MRI (rfMRI). The group specific RSNs were identified by high model order independent component analysis (ICA) and a dual regression technique was used to detect between-group differences in the RSNs with p < 0.05 threshold corrected for multiple comparisons. A y-concatenation method was used to correct for multiple comparisons for multiple independent components, gray matter differences as well as the voxel level. We found increased connectivity in several networks within patients with bvFTD compared to the control group. The most prominent enhancement was seen in the right frontotemporal area and insula. A significant increase in functional connectivity was also detected in the left dorsal attention network (DAN), in anterior paracingulate—a default mode sub-network as well as in the anterior parts of the frontal pole. Notably the increased patterns of connectivity were seen in areas around atrophic regions. The present results demonstrate abnormal increased connectivity in several important brain networks including the DAN and default-mode network (DMN) in patients with bvFTD. These changes may be associated with decline in executive functions and attention as well as apathy, which are the major cognitive and neuropsychiatric defects in patients with frontotemporal dementia.

The influence of the amplitude of low-frequency fluctuations on resting-state functional connectivity

Studies of brain functional connectivity have provided a better understanding of organization and integration of large-scale brain networks. Functional connectivity using resting-state functional magnetic resonance imaging (fMRI) is typically based upon the correlations of the low-frequency fluctuation of fMRI signals. Reproducible spatial maps in the brain have also been observed using the amplitude of low-frequency fluctuations (ALFF) in resting-state. However, little is known about the influence of the ALFF on the functional connectivity measures. In the present study, we analyzed resting-state fMRI data on 79 healthy old individuals. Spatial independent component analysis and regions of interest (ROIs) based connectivity analysis were performed to obtain measures of functional connectivity. ALFF maps were also calculated. First, voxel-matched inter-subject correlations were computed between back-reconstructed IC and ALFF maps. For all the resting-state networks, there was a consistent correlation between ALFF variability and network strengths (within regions that had high IC strengths). Next, inter-subject variance of correlations across 160 functionally defined ROIs were correlated with the corresponding ALFF variance. The connectivity of several ROIs to other regions were more likely to correlate with its own regional ALFF. These regions were mainly located in the anterior cingulate cortex, medial prefrontal cortex, precuneus, insula, basal ganglia, and thalamus. These associations may suggest a functional significance of functional connectivity modulations. Alternatively, the fluctuation amplitudes may arise from physiological noises, and therefore, need to be controlled when studying resting-state functional connectivity.