Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale ...thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length), and a dynamic sense, computed using sliding windows (44 s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical-subcortical antagonism (anti-correlations) and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivity with sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences.
Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional ...networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns.
We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.
Schizophrenia (SZ) is a severe neuropsychiatric disorder associated with disrupted connectivity within the thalamic-cortico-cerebellar network. Resting-state functional connectivity studies have ...reported thalamic hypoconnectivity with the cerebellum and prefrontal cortex as well as thalamic hyperconnectivity with sensory cortical regions in SZ patients compared with healthy comparison participants (HCs). However, fundamental questions remain regarding the clinical significance of these connectivity abnormalities.
Resting state seed-based functional connectivity was used to investigate thalamus to whole brain connectivity using multi-site data including 183 SZ patients and 178 matched HCs. Statistical significance was based on a voxel-level FWE-corrected height threshold of p < 0.001. The relationships between positive and negative symptoms of SZ and regions of the brain demonstrating group differences in thalamic connectivity were examined.
HC and SZ participants both demonstrated widespread positive connectivity between the thalamus and cortical regions. Compared with HCs, SZ patients had reduced thalamic connectivity with bilateral cerebellum and anterior cingulate cortex. In contrast, SZ patients had greater thalamic connectivity with multiple sensory-motor regions, including bilateral pre- and post-central gyrus, middle/inferior occipital gyrus, and middle/superior temporal gyrus. Thalamus to middle temporal gyrus connectivity was positively correlated with hallucinations and delusions, while thalamus to cerebellar connectivity was negatively correlated with delusions and bizarre behavior.
Thalamic hyperconnectivity with sensory regions and hypoconnectivity with cerebellar regions in combination with their relationship to clinical features of SZ suggest that thalamic dysconnectivity may be a core neurobiological feature of SZ that underpins positive symptoms.
Background: The Functional Imaging Biomedical Informatics Network is a consortium developing methods for multisite functional imaging studies. Both prefrontal hyper- or hypoactivity in chronic ...schizophrenia have been found in previous studies of working memory. Methods: In this functional magnetic resonance imaging (fMRI) study of working memory, 128 subjects with chronic schizophrenia and 128 age- and gender-matched controls were recruited from 10 universities around the United States. Subjects performed the Sternberg Item Recognition Paradigm1,2 with memory loads of 1, 3, or 5 items. A region of interest analysis examined the mean BOLD signal change in an atlas-based demarcation of the dorsolateral prefrontal cortex (DLPFC), in both groups, during both the encoding and retrieval phases of the experiment over the various memory loads. Results: Subjects with schizophrenia performed slightly but significantly worse than the healthy volunteers and showed a greater decrease in accuracy and increase in reaction time with increasing memory load. The mean BOLD signal in the DLPFC was significantly greater in the schizophrenic group than the healthy group, particularly in the intermediate load condition. A secondary analysis matched subjects for mean accuracy and found the same BOLD signal hyperresponse in schizophrenics. Conclusions: The increase in BOLD signal change from minimal to moderate memory loads was greater in the schizophrenic subjects than in controls. This effect remained when age, gender, run, hemisphere, and performance were considered, consistent with inefficient DLPFC function during working memory. These findings from a large multisite sample support the concept not of hyper- or hypofrontality in schizophrenia, but rather DLPFC inefficiency that may be manifested in either direction depending on task demands. This redirects the focus of research from direction of difference to neural mechanisms of inefficiency.
Parents of children with an autism spectrum disorder (ASD) show subtle deficits in aspects of social behavior and face processing, which resemble those seen in ASD, referred to as the "Broad Autism ...Phenotype " (BAP). While abnormal activation in ASD has been reported in several brain structures linked to social cognition, little is known regarding patterns in the BAP. We compared autism parents with control parents with no family history of ASD using 2 well-validated face-processing tasks. Results indicated increased activation in the autism parents to faces in the amygdala (AMY) and the fusiform gyrus (FG), 2 core face-processing regions. Exploratory analyses revealed hyper-activation of lateral occipital cortex (LOC) bilaterally in autism parents with aloof personality ("BAP+"). Findings suggest that abnormalities of the AMY and FG are related to underlying genetic liability for ASD, whereas abnormalities in the LOC and right FG are more specific to behavioral features of the BAP. Results extend our knowledge of neural circuitry underlying abnormal face processing beyond those previously reported in ASD to individuals with shared genetic liability for autism and a subset of genetically related individuals with the BAP.
Reward system dysfunction is evident across neuropsychiatric conditions. Here we present data from a double-blinded pharmaco-fMRI study investigating the triggering of anhedonia and reward circuit ...activity in women.
The hormonal states of pregnancy and parturition were simulated in euthymic women with a history of postpartum depression (PPD+; n = 15) and those without such a history (PPD−; n = 15) by inducing hypogonadism, adding back estradiol and progesterone for 8 weeks (“addback”), and then withdrawing both steroids (“withdrawal”). Anhedonia was assessed using the Inventory of Depression and Anxiety Symptoms (IDAS) during each hormone phase. Those who reported a 30 % or greater increase in IDAS anhedonia, dysphoria, or ill temper during addback or withdrawal, compared with pre-treatment, were identified as hormone sensitive (HS+) and all others were identified as non-hormone sensitive (HS−). The monetary incentive delay (MID) task was administered during fMRI sessions at pre-treatment and during hormone withdrawal to assess brain activation during reward anticipation and feedback.
On average, anhedonia increased during addback and withdrawal in PPD+ but not PPD−. During reward feedback, both HS+ (n = 10) and HS− (n = 18) showed decreased activation in clusters in the right putamen (p < .031, FWE-corrected) and left postcentral and supramarginal gyri (p < .014, FWE-corrected) at the withdrawal scans, relative to pre-treatment scans.
A modest sample size, stringent exclusion criteria, and relative lack of diversity in study participants limit the generalizability of results.
Although results do not explain differential hormone sensitivity in depression, they demonstrate significant effects of reproductive hormones on reward-related brain function in women.
•Estradiol and progesterone administration triggered depression symptoms in women with a history of postpartum depression.•Estradiol and progesterone administration also decreased activity in brain areas that respond to rewards.•Changes in brain activity in the reward network caused by pregnancy hormones did not explain why some women experience postpartum depression while others do not.
The past 10 years have seen an explosion of approaches that focus on the study of time-resolved change in functional connectivity (FC). FC characterization among networks at a whole-brain level is ...frequently termed functional network connectivity (FNC). Time-resolved or dynamic functional network connectivity (dFNC) focuses on the estimation of transient, recurring, whole-brain patterns of FNC. While most approaches in this area have attempted to capture dynamic linear correlation, we are particularly interested in whether explicitly nonlinear relationships, above and beyond linear, are present and contain unique information. This study thus proposes an approach to assess explicitly nonlinear dynamic functional network connectivity (EN dFNC) derived from the relationship among independent component analysis time courses. Linear relationships were removed at each time point to evaluate, typically ignored, explicitly nonlinear dFNC using normalized mutual information (NMI). Simulations showed the proposed method estimated explicitly nonlinearity over time, even within relatively short windows of data. We then, applied our approach on 151 schizophrenia patients, and 163 healthy controls fMRI data and found three unique, highly structured, mostly long-range, functional states that also showed significant group differences. In particular, explicitly nonlinear relationships tend to be more widespread than linear ones. Results also highlighted a state with long range connections to the visual domain, which were significantly reduced in schizophrenia. Overall, this work suggests that quantifying EN dFNC may provide a complementary and potentially valuable tool for studying brain function by exposing relevant variation that is typically ignored.
Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence ...limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.
Abstract The motor deficits in Parkinson’s disease (PD) have been primarily associated with internally guided (IG), but not externally guided (EG), tasks. This study investigated the functional ...mechanisms underlying this phenomenon using genetically-matched twins. Functional magnetic resonance images were obtained from a monozygotic twin pair discordant for clinical PD. Single-photon emission computed tomography neuroimaging using 123 I(−)-2-β-carboxymethoxy-3-β-(4-iodophenyl)tropane confirmed their disease-discordant status by demonstrating a severe loss of transporter binding in the PD-twin, whereas the non-PD-twin was normal. Six runs of functional magnetic resonance imaging (fMRI) data were acquired from each twin performing EG and IG right-hand finger sequential tasks. The percentage of voxels activated in each of several regions of interest (ROI) was calculated. Multiple analysis of variance was used to compare each twin’s activity in ROIs constituting the striato-thalamo-cortical motor circuits basal ganglia (BG)-cortical circuitry, but including the globus pallidus/putamen, thalamus, supplementary motor area, and primary motor cortex and cerebello-thalamo-cortical circuits (cerebellar–cortical circuitry, including the cerebellum, thalamus, somatosensory cortex, and lateral premotor cortex). During the EG task, there were no significant differences between the twins in bilateral BG–cortical pathways, either basally or after levodopa, whereas the PD-twin had relatively increased activity in the cerebellar–cortical pathways basally that was normalized by levodopa. During the IG task, the PD-twin had less activation than the non-PD-twin in ROIs of the bilateral BG–cortical and cerebellar–cortical pathways. Levodopa normalized the hypoactivation in the contralateral BG–cortical pathway, but “over-corrected” the activation in the ipsilateral BG–cortical and bilateral cerebellar–cortical pathways. In this first fMRI study of twins discordant for PD, the data support the hypothesis that BG–cortical and cerebellar–cortical pathways are task-specifically influenced by PD. The levodopa-induced “over-activation” of BG–cortical and cerebellar–cortical pathways, and its relevance to both compensatory changes in PD and the long-term effects of levodopa in PD, merit further exploration.
The electroencephalogram (EEG) recorded from the human scalp is widely used to study cognitive and brain functions in schizophrenia. Current research efforts are primarily devoted to the assessment ...of event-related potentials (ERPs) and event-related oscillations (EROs), extracted from the ongoing EEG, in patients with schizophrenia and in clinically unaffected individuals who, due to their family history and current mental status, are at high risk for developing schizophrenia. In this article, we discuss the potential usefulness of ERPs and EROs as genetic vulnerability markers, as pathophysiological markers, and as markers of possible ongoing progressive cognitive and cortical deterioration in schizophrenia. Our main purpose is to illustrate that these neurophysiological measures can offer valuable quantitative biological markers of basic pathophysiological mechanisms and cognitive dysfunctions in schizophrenia, yet they may not be specific to current psychiatry's diagnosis and classification. These biological markers can provide unique information on the nature and extent of cognitive and brain dysfunction in schizophrenia. Moreover, they can be utilized to gain deeper theoretical insights into illness etiology and pathophysiology and may lead to improvements in early detection and more effective and targeted treatment of schizophrenia. We conclude by addressing several key methodological, conceptual, and interpretative issues involved in this research field and by suggesting future research directions.
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