IQ-related fMRI differences during cognitive set shifting

Perception of social inclusion/exclusion and response inhibition in adolescents with past suicide attempt: a multidomain task-based fMRI study

The occurrence of suicidal behaviors increases during adolescence. Hypersensitivity to negative social signals and deficits in cognitive control are putative mechanisms of suicidal behaviors, which necessitate confirmation in youths. Multidomain functional neuroimaging could enhance the identification of patients at suicidal risk beyond standard clinical measures. Three groups of adolescents (N = 96; 78% females, age = 11.6-18.1) were included: patients with depressive disorders and previous suicide attempts (SA, n = 29); patient controls with depressive disorders but without any suicide attempt history (PC, n = 35); and healthy controls (HC, n = 32). We scanned participants with 3T-MRI during social inclusion/exclusion (Cyberball Game) and response inhibition (Go-NoGo) tasks. Neural activation was indexed by the blood-oxygenation-level dependent (BOLD) of the hemodynamic response during three conditions in the Cyberball Game ("Control condition", "Social Inclusion", and "Social Exclusion"), and two conditions in Go-NoGo task ("Go" and "NoGo" blocks). ANCOVA-style analysis identified group effects across three whole-brain contrasts: 1) NoGo vs. Go, 2) Social inclusion vs. control condition, 3) Social exclusion vs. control condition. We found that SA had lower activation in the left insula during social inclusion vs. control condition compared to PC and HC. Moreover, SA compared to PC had higher activity in the right middle prefrontal gyrus during social exclusion vs. control condition, and in bilateral precentral gyri during NoGo vs. Go conditions. Task-related behavioral and self-report measures (Self-reported emotional reactivity in the Cyberball Game, response times and number of errors in the Go-NoGo Task) did not discriminate groups. In conclusion, adolescent suicidal behaviors are likely associated with neural alterations related to the processing of social perception and response inhibition. Further research, involving prospective designs and diverse cohorts of patients, is necessary to explore the potential of neuroimaging as a tool in understanding the emergence and progression of suicidal behaviors.

BrainSuite BIDS App: Containerized Workflows for MRI Analysis

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The BrainSuite Anatomical Pipeline (BAP) extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The BrainSuite Diffusion Pipeline (BDP) processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for geometric image distortion, and fitting diffusion models to the DWI data. The BrainSuite Functional Pipeline (BFP) performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. BFP coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. Each of these outputs can then be processed during group-level analysis. The outputs of BAP and BDP are analyzed using the BrainSuite Statistics in R (bssr) toolbox, which provides functionality for hypothesis testing and statistical modeling. The outputs of BFP can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

Investigating the neural correlates of affective mentalizing and their association with general intelligence in patients with schizophrenia

BACKGROUND AND HYPOTHESIS

Mentalizing impairment in schizophrenia has been linked to altered neural responses. This study aimed to replicate previous findings of altered activation of the mentalizing network in schizophrenia and investigate its possible association with impaired domain-general cognition.

STUDY DESIGN

We analyzed imaging data from two large multi-centric German studies including 64 patients, 64 matched controls and a separate cohort of 300 healthy subjects, as well as an independent Australian study including 46 patients and 61 controls. All subjects underwent functional magnetic resonance imaging while performing the same affective mentalizing task and completed a cognitive assessment battery. Group differences in activation of the mentalizing network were assessed by classical as well as Bayesian two-sample t-tests. Multiple regression analysis was performed to investigate effects of neurocognitive measures on activation of the mentalizing network.

STUDY RESULTS

We found no significant group differences in activation of the mentalizing network. Bayes factors indicate that these results provide genuine evidence for the null hypothesis. We found a positive association between verbal intelligence and activation of the medial prefrontal cortex, a key region of the mentalizing network, in three independent samples. Finally, individuals with low verbal intelligence showed altered activation in areas previously implicated in mentalizing dysfunction in schizophrenia.

CONCLUSIONS

Mentalizing activation in patients with schizophrenia might not differ compared to large well-matched groups of healthy controls. Verbal intelligence is an important confounding variable in group comparisons, which should be considered in future studies of the neural correlates of mentalizing dysfunction in schizophrenia.

Neural processes of inhibitory control in American Indian peoples are associated with reduced mental health problems

American Indians (AI) experience disproportionately high prevalence of suicide and substance use disorders (SUD). However, accounting for risk burden (e.g. historical trauma and discrimination), the likelihood of mental health disorders or SUD is similar or decreased compared with the broader population. Such findings have spurred psychological research examining the protective factors, but no studies have investigated its potential neural mechanisms. Inhibitory control is one of the potential neurobehavioral construct with demonstrated protective effects, but has not been examined in neuroimaging studies with AI populations specifically. We examined the incidence of suicidal thoughts and behaviors (STB) and SUD among AI (n = 76) and propensity matched (sex, age, income, IQ proxy and trauma exposure) non-Hispanic White (NHW) participants (n = 76). Among the AI sample, functional magnetic resonance imaging (fMRI) data recorded during the stop-signal task (SST) was examined in relation to STB and SUDs. AIs relative to NHW subjects displayed lower incidence of STB. AIs with no reported STBs showed greater activity in executive control regions during the SST compared with AI who endorsed STB. AI without SUD demonstrated lower activity relative to those individual reporting SUD. Results are consistent with a growing body of literature demonstrating the high level of risk burden driving disparate prevalence of mental health concerns in AI. Furthermore, differential activation during inhibitory control processing in AI individuals without STB may represent a neural mechanism of protective effects against mental health problems in AI. Future research is needed to elucidate sociocultural factors contributing protection against mental health outcomes in AIs and further delineate neural mechanisms with respect to specific concerns (e.g. SUD vs STB).

Long term effects of red wine consumption in brain: an MRI, fMRI and neuropsychological evaluation study

Red wine (RW) consumption has been proposed to have a potential health benefit. However, the effect of RW consumption on the brain is not entirely known, mainly when associated with aging. Regular red wine consumers (n = 30) and abstainers (ABST; n = 27) without cognitive impairment were evaluated for brain structural characteristics (Fazekas score and voxel-based morphometry) and for functional adaptations assessed by fMRI (using the Word Tasks Color Stroop (WCST) and Two-Back (TBT)), as well as by neuropsychological tests in different domains. There were no significant differences regarding brain morphological features. RW consumers showed greater activation in the thalamus during WCST and in paracingulate/anterior cingulate cortices, left superior frontal gyrus and frontal pole during TBT. ABST required higher activation of different cortical areas in the left parietal lobe during WCST. Age and intelligence quotient influenced those activations. In Stroop and trail-making neuropsychological tests, RW consumers performed slightly better than ABST. This study should be viewed as hypothesis-generating rather than conclusive.

Processing Speed Predicts Mean Performance in Task-Switching but Not Task-Switching Cost

In several studies, it has been suggested that task-switching performance is linked to processing speed. Here we argue that the relation between processing speed and high-level cognitive ability found in previous studies may be due to confounded measurements of processing speed and task-switching ability. In the present study we required participants to complete an inspection time (IT) task to probe their processing speed. We employed conventional task-switching paradigms but applied a linear integrated speed-accuracy score (LISAS) which combines latency and accuracy scores to express task-switching ability. The results of regression analyses show that IT predicted average performance in task-switching paradigms. However, IT did not relate to any specific effects common in the task-switching task, which contradicts previous results. Our results suggest independent mechanisms of processing speed and tasks that require a high level of cognitive flexibility and control.

Multi-Paradigm fMRI Fusion via Sparse Tensor Decomposition in Brain Functional Connectivity Study

Functional magnetic resonance imaging (fMRI) is a powerful technique with the potential to estimate individual variations in behavioral and cognitive traits. Joint learning of multiple datasets can utilize their complementary information so as to improve learning performance, but it also gives rise to the challenge for data fusion to effectively integrate brain patterns elicited by multiple fMRI data. However, most of the current data fusion methods analyze each single dataset separately and further infer the relationship among them, which fail to utilize the multidimensional structure inherent across modalities and may ignore complex but important interactions. To address this issue, we propose a novel sparse tensor decomposition method to integrate multiple task-stimulus (paradigm) fMRI data. Seeing each paradigm fMRI as one modality, our proposed method considers the relationships across subjects and modalities simultaneously. In specific, a third-order tensor is first modeled by using the functional network connectivity (FNC) of subjects in multiple fMRI paradigms. A novel sparse tensor decomposition with the regularization terms is designed to factorize the tensor into a series of rank-one components, which can extract the shared components across modalities as the embedded features. The L2,1-norm regularizer (i.e., group sparsity) is enforced to select a few common features among multiple subjects. Validation of the proposed method is performed on realistic three paradigm fMRI datasets from the Philadelphia Neurodevelopmental Cohort (PNC) study, for the study of the relationship between the FNC and human cognitive abilities. Experimental results show our method outperforms several other competing methods in the prediction of individuals with different cognitive behaviors via the wide range achievement test (WRAT). Furthermore, our method discovers the FNC related to the cognitive behaviors, such as the connectivity associated with the default mode network (DMN) for three paradigms, and the connectivity between DMN and visual (VIS) domains within the emotion task.

Multimodal data revealed different neurobiological correlates of intelligence between males and females

Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design.

Cortical and subcortical responsiveness to intensive adaptive working memory training: An MRI surface-based analysis

Working memory training (WMT) has been shown to have effects on cognitive performance, the precise effects and the underlying neurobiological mechanisms are, however, still a matter of debate. In particular, the impact of WMT on gray matter morphology is still rather unclear. In the present study, 59 healthy middle‐aged participants (age range 50–65 years) were pseudo‐randomly single‐blinded allocated to an 8‐week adaptive WMT or an 8‐week nonadaptive intervention. Before and after the intervention, high resolution magnetic resonance imaging (MRI) was performed and cognitive test performance was assessed in all participants. Vertex‐wise cortical volume, thickness, surface area, and cortical folding was calculated. Seven subcortical volumes of interest and global mean cortical thickness were also measured. Comparisons of symmetrized percent change (SPC) between groups were conducted to identify group by time interactions. Greater increases in cortical gyrification in bilateral parietal regions, including superior parietal cortex and inferior parietal lobule as well as precuneus, greater increases in cortical volume and thickness in bilateral primary motor cortex, and changes in surface area in bilateral occipital cortex (medial and lateral occipital cortex) were detected in WMT group after training compared to active controls. Structural training‐induced changes in WM‐related regions, especially parietal regions, might provide a better brain processing environment for higher WM load.

Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data

Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman's general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffusion-Weighted Imaging (DWI) and functional Magnetic Resonance Imaging (fMRI) has allowed researchers to test hypotheses about neural correlates of intelligence in humans.This review summarizes recent findings on the associations of human intelligence with neuroimaging data. To this end, first, we review the literature that has related brain morphometry to intelligence. Next, we elaborate on the applications of DWI and restingstate fMRI on the investigation of intelligence. Then, we provide a survey of literature that has used multimodal DWI-fMRI to shed light on intelligence. Finally, we discuss the state-of-the-art of individualized prediction of intelligence from neuroimaging data and point out future strategies. Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence.

The Relationship Between General Intelligence and Cortical Structure in Healthy Individuals

Considerable work in recent years has examined the relationship between cortical thickness (CT) and general intelligence (IQ) in healthy individuals. It is not known whether specific IQ variables (i.e., perceptual reasoning [PIQ], verbal comprehension IQ [VIQ], and full-scale IQ [FSIQ]) are associated with multiple cortical measures (i.e., CT, cortical volume (CV), cortical surface area (CSA) and cortical gyrification (CG)) within the same individuals. Here we examined the association between these neuroimaging metrics and IQ in 56 healthy adults. At a cluster-forming threshold (CFT) of p < 0.05, we observed significant positive relationships between CT and all three IQ variables in regions within the posterior frontal and superior parietal lobes. Regions within the temporal and posterior frontal lobes exhibited positive relationships between CV and two IQ variables (PIQ and FSIQ) and regions within the inferior parietal lobe exhibited positive relationships between CV and PIQ. Additionally, CV was positively associated with VIQ in the left insula and with FSIQ within the inferior frontal gyrus. At a more stringent CFT (p < 0.01), the CT-PIQ, CT-VIQ, CT-FSIQ, and CV-PIQ relationships remained significant within the posterior frontal lobe, as did the CV-PIQ relationship within the temporal and inferior parietal lobes. We did not observe statistically significant relationships between IQ and either CSA or CG. Our findings suggest that the neural basis of IQ extends beyond previously observed relationships with fronto-parietal regions. We also conclude that CT and CV may be more useful metrics than CSA or CG in the study of intellectual abilities.

Physiological substrates of executive functioning: a systematic review of the literature

Executive function (EF) is a multifaceted construct that has been defined as a set of higher-order cognitive processes that allow for flexibility, self-regulation, strategic planning, and goal-directed behaviors. EFs have been studied in numerous clinical disorders using a variety of neuropsychological tasks and, more recently, neuroimaging techniques. The underlying physiological substrates of EF were historically attributed to the frontal lobes; however, recent studies suggest more widespread involvement of additional brain regions. The purpose of the present study was to conduct a systematic review (using PRISMA 2009 guidelines) of neuroimaging studies employing functional magnetic resonance imaging and diffusion tensor imaging methods investigating the physiological substrates of EFs in attention-deficit/hyperactivity disorder compared to other clinical groups and non-clinical participants. Research articles were retrieved using PsycINFO, PsycARTICLES, MEDLINE, and ScienceDirect, beginning February 2015 through May 2016. A total of 42 studies met eligibility. Of those 42 studies, 22 studies included clinical participants and 20 studies included non-clinical participants. Results revealed increased activation of the frontal brain region in the majority of non-clinical studies and approximately 50% of the clinical studies, albeit with some inconsistencies across subregions, tasks, and age groups. Implications, methodological limitations, and suggestions for future research are discussed.

Disentangling the autism-anxiety overlap: fMRI of reward processing in a community-based longitudinal study

Up to 40% of youth with autism spectrum disorder (ASD) also suffer from anxiety, and this comorbidity is linked with significant functional impairment. However, the mechanisms of this overlap are poorly understood. We investigated the interplay between ASD traits and anxiety during reward processing, known to be affected in ASD, in a community sample of 1472 adolescents (mean age=14.4 years) who performed a modified monetary incentive delay task as part of the Imagen project. Blood-oxygen-level dependent (BOLD) responses to reward anticipation and feedback were compared using a 2x2 analysis of variance test (ASD traits: low/high; anxiety symptoms: low/high), controlling for plausible covariates. In addition, we used a longitudinal design to assess whether neural responses during reward processing predicted anxiety at 2-year follow-up. High ASD traits were associated with reduced BOLD responses in dorsal prefrontal regions during reward anticipation and negative feedback. Participants with high anxiety symptoms showed increased lateral prefrontal responses during anticipation, but decreased responses following feedback. Interaction effects revealed that youth with combined ASD traits and anxiety, relative to other youth, showed high right insula activation when anticipating reward, and low right-sided caudate, putamen, medial and lateral prefrontal activations during negative feedback (all clusters PFWE<0.05). BOLD activation patterns in the right dorsal cingulate and right medial frontal gyrus predicted new-onset anxiety in participants with high but not low ASD traits. Our results reveal both quantitatively enhanced and qualitatively distinct neural correlates underlying the comorbidity between ASD traits and anxiety. Specific neural responses during reward processing may represent a risk factor for developing anxiety in ASD youth.

Response inhibition and interference control in patients with bipolar I disorder and first-degree relatives

OBJECTIVES

The current study aimed to assess both response inhibition (RI) and interference control (IC) in euthymic patients with bipolar disorder (BD-Ps) as well as asymptomatic first-degree relatives (BD-Rs) and healthy controls (HCs) in order to evaluate trait-as opposed to illness-associated features of these components.

METHODS

BD-Ps (n = 35) who had been in the euthymic state for at least six months, BD-Rs (n = 30), and HCs (n = 33) completed a Stop-Signal Task (SST) and Stroop Task to assess RI and IC, respectively. Groups were compared on the stop-signal reaction time (SSRT), stop-signal delay (SSD), mean reaction time on go trials (go-RT), Stroop interference score (S-interference), and number of errors on the color-word-naming trial (S-error). Associations between the patient's clinical features and RI and IC, between the patient's treatment and RI and IC, and between RI and IC in each group were investigated.

RESULTS

BD-Ps and BD-Rs had significantly shorter go-RT and SSD, and longer SSRT compared to HCs, with these scores being similar between the BD-Ps and BD-Rs. Also, both BD-Ps and BD-Rs made significantly more S-errors than HCs, whereas, the S-interference score was not significantly different between groups. There were no significant correlations between Stroop Task and SST scores within each group, nor between clinical features or treatment variables and RI and IC in BD-Ps.

CONCLUSIONS

Overall, impairment in RI and IC (only on S-error score) was present in both patients and relatives. The persistence of these deficits in the absence of mood symptoms suggests that these features may represent candidate endophenotypes for bipolar disorder.

Correlates of Near-Infrared Spectroscopy Brain-Computer Interface Accuracy in a Multi-Class Personalization Framework

Brain–computer interfaces (BCIs) provide individuals with a means of interacting with a computer using only neural activity. To date, the majority of near-infrared spectroscopy (NIRS) BCIs have used prescribed tasks to achieve binary control. The goals of this study were to evaluate the possibility of using a personalized approach to establish control of a two-, three-, four-, and five-class NIRS–BCI, and to explore how various user characteristics correlate to accuracy. Ten able-bodied participants were recruited for five data collection sessions. Participants performed six mental tasks and a personalized approach was used to select each individual’s best discriminating subset of tasks. The average offline cross-validation accuracies achieved were 78, 61, 47, and 37% for the two-, three-, four-, and five-class problems, respectively. Most notably, all participants exceeded an accuracy of 70% for the two-class problem, and two participants exceeded an accuracy of 70% for the three-class problem. Additionally, accuracy was found to be strongly positively correlated (Pearson’s) with perceived ease of session (ρ = 0.653), ease of concentration (ρ = 0.634), and enjoyment (ρ = 0.550), but strongly negatively correlated with verbal IQ (ρ = −0.749).

Antagonistic neural networks underlying differentiated leadership roles

The emergence of two distinct leadership roles, the task leader and the socio-emotional leader, has been documented in the leadership literature since the 1950s. Recent research in neuroscience suggests that the division between task-oriented and socio-emotional-oriented roles derives from a fundamental feature of our neurobiology: an antagonistic relationship between two large-scale cortical networks – the task-positive network (TPN) and the default mode network (DMN). Neural activity in TPN tends to inhibit activity in the DMN, and vice versa. The TPN is important for problem solving, focusing of attention, making decisions, and control of action. The DMN plays a central role in emotional self-awareness, social cognition, and ethical decision making. It is also strongly linked to creativity and openness to new ideas. Because activation of the TPN tends to suppress activity in the DMN, an over-emphasis on task-oriented leadership may prove deleterious to social and emotional aspects of leadership. Similarly, an overemphasis on the DMN would result in difficulty focusing attention, making decisions, and solving known problems. In this paper, we will review major streams of theory and research on leadership roles in the context of recent findings from neuroscience and psychology. We conclude by suggesting that emerging research challenges the assumption that role differentiation is both natural and necessary, in particular when openness to new ideas, people, emotions, and ethical concerns are important to success.

The effect of intellectual ability on functional activation in a neurodevelopmental disorder: preliminary evidence from multiple fMRI studies in Williams syndrome

Background

Williams syndrome (WS) is a rare genetic disorder caused by the deletion of approximately 25 genes at 7q11.23 that involves mild to moderate intellectual disability (ID). When using functional magnetic resonance imaging (fMRI) to compare individuals with ID to typically developing individuals, there is a possibility that differences in IQ contribute to between-group differences in BOLD signal. If IQ is correlated with BOLD signal, then group-level analyses should adjust for IQ, or else IQ should be matched between groups. If, however, IQ is not correlated with BOLD signal, no such adjustment or criteria for matching (and exclusion) based on IQ is necessary.

Methods

In this study, we aimed to test this hypothesis systematically using four extant fMRI datasets in WS. Participants included 29 adult subjects with WS (17 men) demonstrating a wide range of standardized IQ scores (composite IQ mean = 67, SD = 17.2). We extracted average BOLD activation for both cognitive and task-specific anatomically defined regions of interest (ROIs) in each individual and correlated BOLD with composite IQ scores, verbal IQ scores and non-verbal IQ scores in Spearman rank correlation tests.

Results

Of the 312 correlations performed, only six correlations (2%) in four ROIs reached statistical significance at a P value < 0.01, but none survived correction for multiple testing. All six correlations were positive. Therefore, none supports the hypothesis that IQ is negatively correlated with BOLD response.

Conclusions

These data suggest that the inclusion of subjects with below normal IQ does not introduce a confounding factor, at least for some types of fMRI studies with low cognitive load. By including subjects who are representative of IQ range for the targeted disorder, findings are more likely to generalize to that population.

Antipsychotic dose and diminished neural modulation: a multi-site fMRI study

BACKGROUND

The effect of antipsychotics on the blood oxygen level dependent signal in schizophrenia is poorly understood. The purpose of the present investigation is to examine the effect of antipsychotic medication on independent neural networks during a motor task in a large, multi-site functional magnetic resonance imaging investigation.

METHODS

Seventy-nine medicated patients with schizophrenia and 114 comparison subjects from the Mind Clinical Imaging Consortium database completed a paced, auditory motor task during functional magnetic resonance imaging (fMRI). Independent component analysis identified temporally cohesive but spatially distributed neural networks. The independent component analysis time course was regressed with a model time course of the experimental design. The resulting beta weights were evaluated for group comparisons and correlations with chlorpromazine equivalents.

RESULTS

Group differences between patients and comparison subjects were evident in the cortical and subcortical motor networks, default mode networks, and attentional networks. The chlorpromazine equivalents correlated with the unimotor/bitemporal (rho=-0.32, P=0.0039), motor/caudate (rho=-0.22, P=0.046), posterior default mode (rho=0.26, P=0.020), and anterior default mode networks (rho=0.24, P=0.03). Patients on typical antipsychotics also had less positive modulation of the motor/caudate network relative to patients on atypical antipsychotics (t(77)=2.01, P=0.048).

CONCLUSION

The results suggest that antipsychotic dose diminishes neural activation in motor (cortical and subcortical) and default mode networks in patients with schizophrenia. The higher potency, typical antipsychotics also diminish positive modulation in subcortical motor networks. Antipsychotics may be a potential confound limiting interpretation of fMRI studies on the disease process in medicated patients with schizophrenia.

Decreased default mode neural modulation with age in schizophrenia

OBJECTIVE

Previous studies have shown that patients with schizophrenia have less modulation of the task-positive and default mode neural networks during novelty detection. The diminished modulation may be interpreted as less functional activation of the task-positive network and less functional deactivation of the default mode network. The relationship between network modulation and age has not been assessed in patients with a long duration of illness.

METHOD

Seventeen patients with schizophrenia (age range, from 34 to 60 years) with minimum disorder duration of 15 years and 28 demographically similar comparison subjects (age range, from 36 to 58 years) from the Mind Clinical Imaging Consortium database completed the auditory oddball discrimination task. Independent component analysis identified temporally cohesive but spatially distributed neural networks.

RESULTS

Group membership (F[1, 41] = 7.17, p = 0.011) and the interaction of group and age (F[1, 41] = 6.92, p = 0.012) affected the modulation of the anterior default mode network. Duration of illness was also significantly related to the modulation of the anterior default mode network (t[2, 15] = 2.24, p <0.042). These results were selective for the anterior default mode network and were not replicated with the posterior default mode network.

CONCLUSIONS

These findings show evidence of changes in the temporal modulation of the anterior default mode network with age and duration of illness in patients with schizophrenia. The loss of negative modulation of the anterior default mode network suggests that neural functions in schizophrenia may not be "static" later in the disease course.

Cognitive control of drug craving inhibits brain reward regions in cocaine abusers

Loss of control over drug taking is considered a hallmark of addiction and is critical in relapse. Dysfunction of frontal brain regions involved with inhibitory control may underlie this behavior. We evaluated whether addicted subjects when instructed to purposefully control their craving responses to drug-conditioned stimuli can inhibit limbic brain regions implicated in drug craving. We used PET and 2-deoxy-2[18F]fluoro-d-glucose to measure brain glucose metabolism (marker of brain function) in 24 cocaine abusers who watched a cocaine-cue video and compared brain activation with and without instructions to cognitively inhibit craving. A third scan was obtained at baseline (without video). Statistical parametric mapping was used for analysis and corroborated with regions of interest. The cocaine-cue video increased craving during the no-inhibition condition (pre 3+/-3, post 6+/-3; p<0.001) but not when subjects were instructed to inhibit craving (pre 3+/-2, post 3+/-3). Comparisons with baseline showed visual activation for both cocaine-cue conditions and limbic inhibition (accumbens, orbitofrontal, insula, cingulate) when subjects purposefully inhibited craving (p<0.001). Comparison between cocaine-cue conditions showed lower metabolism with cognitive inhibition in right orbitofrontal cortex and right accumbens (p<0.005), which was associated with right inferior frontal activation (r=-0.62, p<0.005). Decreases in metabolism in brain regions that process the predictive (nucleus accumbens) and motivational value (orbitofrontal cortex) of drug-conditioned stimuli were elicited by instruction to inhibit cue-induced craving. This suggests that cocaine abusers may retain some ability to inhibit craving and that strengthening fronto-accumbal regulation may be therapeutically beneficial in addiction.