Higher-Order Intrinsic Brain Network Trajectories after Antipsychotic Treatment in Medication-Naïve First Episode Psychosis Patients

BACKGROUND

Intrinsic brain network connectivity is already altered in first-episode psychosis (FEP), but the longitudinal trajectories of network connectivity especially in response to antipsychotic treatment remain poorly understood. The goal of this study was to investigate how antipsychotic medications affect higher-order intrinsic brain network connectivity in FEP.

METHODS

Data from 87 antipsychotic medication-naïve FEP subjects and 87 healthy controls (HC) were used. Medication-naïve patients received antipsychotic treatment for sixteen weeks. Resting state functional connectivity (FC) of the default mode (DMN), salience (SN), dorsal attention (DAN), and executive control network (ECN) was assessed prior to treatment, six, and sixteen weeks after treatment. We evaluated baseline and FC changes using linear mixed models to test group by time interactions within each network. Associations between FC changes after sixteen weeks and response to treatment were also evaluated.

RESULTS

Prior to treatment significant group differences in all networks were found. However, significant trajectory changes in FC were only found in DMN and ECN. Changes in FC in these networks were associated with treatment response. Several sensitivity analyses showed a consistent normalization of ECN FC in response to antipsychotic treatment.

CONCLUSIONS

Here, we found that alterations in intrinsic brain network FC not only were alleviated with antipsychotic treatment, the extent of this normalization was also associated with the degree of reduction in symptom severity. Together, our data suggests modulation of intrinsic brain network connectivity (mainly fronto-parietal connectivity) as a mechanism underlying antipsychotic treatment response in FEP.

The Different Impact of Depressive or Manic First-episode on Pediatric Bipolar Disorder Patients: Evidence From Resting-state fMRI

Bipolar disorder may begin as depression or mania, which can affect the treatment and prognosis of bipolar disorder. However, the physiological and pathological differences of pediatric bipolar disorder (PBD) patients with different onset symptoms are not clear. The purpose of this study was to investigate the differences of clinical, cognitive function and intrinsic brain networks in PBD patients with first-episode depression and first-episode mania. A total of 63 participants, including 43 patients and 20 healthy controls, underwent resting-state fMRI scans. PBD patients were classified as first-episode depressive and first-episode manic based on their first-episode symptoms. Cognitive tests were used to measure attention and memory of all participants. Independent component analysis (ICA) was used to extract the salience network (SN), default-mode network (DMN), central executive network (ECN) and limbic network (LN) for each participant. Spearman rank correlation analysis was performed between abnormal activation and clinical and cognitive measures. The results showed that there were differences in cognitive functions such as attention and visual memory between first-episode depression and mania, as well as differences activation in anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), precuneus, inferior parietal cortex and parahippocampus. And significant associations of brain activity with clinical assessments or cognition were found in different patients. In conclusion, we found differential impairments in cognitive and brain network activation in first-episode depressive and first-episode manic PBD patients, and correlations were found between these impairments. These evidences may shed light on the different developmental paths of bipolar disorder.

Unraveling the Mysteries of Mental Illness With Psilocybin

Current medications have not been effective in reducing the prevalence of mental illness worldwide. The prevalence of illnesses such as treatment-resistant depression has increased despite the widespread use of a broad set of psychopharmaceuticals. Transcranial magnetic stimulation and ketamine therapy are making great strides in improving treatment-resistant depression outcomes but they have limitations. New psychotherapeutics are required that specifically target the underlying cellular pathologies leading to neuronal atrophy. This neuronal atrophy model is supplanting the long-held neurotransmitter deficit hypothesis to explain mental illness. Interest in psychedelics as therapeutic molecules to treat mental illness is experiencing a 21st-century reawakening that is on the cusp of a transformation. Psilocybin is a pro-drug, found in various naturally occurring mushrooms, that is dephosphorylated to produce psilocin, a classic tryptamine psychedelic functional as a 5-hydroxytryptamine 2A receptor agonist. We have focused this review to include studies in the last two years that suggest psilocybin promotes neuronal plasticity, which may lead to changes in brain network connectivity. Recent advancements in clinical trials using pure psilocybin in therapy suggest that it may effectively relieve the symptoms of depression in patients diagnosed with major depressive disorder and treatment-resistant depression. Sophisticated cellular and molecular experiments at the systems level have produced evidence that demonstrates psilocybin promotes neuritogenesis in the mouse brain - a mechanism that may address the root cause of depression at the cellular level. Finally, studies with psilocybin therapy for major depressive disorder suggest that this ancient molecule can promote functionally connected intrinsic networks in the human brain, resulting in durable improvements in the severity of depressive symptoms. Although further research is necessary, the prospect of using psilocybin for the treatment of mental illness is an enticing possibility.

Individual Differences in Intrinsic Brain Networks Predict Symptom Severity in Autism Spectrum Disorders

The neurobiology of heterogeneous neurodevelopmental disorders such as Autism Spectrum Disorders (ASD) is still unknown. We hypothesized that differences in subject-level properties of intrinsic brain networks were important features that could predict individual variation in ASD symptom severity. We matched cases and controls from a large multicohort ASD dataset (ABIDE-II) on age, sex, IQ, and image acquisition site. Subjects were matched at the individual level (rather than at group level) to improve homogeneity within matched case-control pairs (ASD: n = 100, mean age = 11.43 years, IQ = 110.58; controls: n = 100, mean age = 11.43 years, IQ = 110.70). Using task-free functional magnetic resonance imaging, we extracted intrinsic functional brain networks using projective non-negative matrix factorization. Intrapair differences in strength in subnetworks related to the salience network (SN) and the occipital-temporal face perception network were robustly associated with individual differences in social impairment severity (T = 2.206, P = 0.0301). Findings were further replicated and validated in an independent validation cohort of monozygotic twins (n = 12; 3 pairs concordant and 3 pairs discordant for ASD). Individual differences in the SN and face-perception network are centrally implicated in the neural mechanisms of social deficits related to ASD.

Structural Brain Architectures Match Intrinsic Functional Networks and Vary across Domains: A Study from 15 000+ Individuals

Brain structural networks have been shown to consistently organize in functionally meaningful architectures covering the entire brain. However, to what extent brain structural architectures match the intrinsic functional networks in different functional domains remains under explored. In this study, based on independent component analysis, we revealed 45 pairs of structural-functional (S-F) component maps, distributing across nine functional domains, in both a discovery cohort (n = 6005) and a replication cohort (UK Biobank, n = 9214), providing a well-match multimodal spatial map template for public use. Further network module analysis suggested that unimodal cortical areas (e.g., somatomotor and visual networks) indicate higher S-F coherence, while heteromodal association cortices, especially the frontoparietal network (FPN), exhibit more S-F divergence. Collectively, these results suggest that the expanding and maturing brain association cortex demonstrates a higher degree of changes compared with unimodal cortex, which may lead to higher interindividual variability and lower S-F coherence.

Reduced Dynamic Interactions Within Intrinsic Functional Brain Networks in Early Blind Patients

Neuroimaging studies in early blind (EB) patients have shown altered connections or brain networks. However, it remains unclear how the causal relationships are disrupted within intrinsic brain networks. In our study, we used spectral dynamic causal modeling (DCM) to estimate the causal interactions using resting-state data in a group of 20 EB patients and 20 healthy controls (HC). Coupling parameters in specific regions were estimated, including the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and inferior parietal lobule (IPC) in the default mode network (DMN); dorsal anterior cingulate cortex (dACC) and bilateral anterior insulae (AI) in the salience network (SN), and bilateral frontal eye fields (FEF) and superior parietal lobes (SPL) within the dorsal attention network (DAN). Statistical analyses found that all endogenous connections and the connections from the mPFC to bilateral IPCs in EB patients were significantly reduced within the DMN, and the effective connectivity from the PCC and lIPC to the mPFC, and from the mPFC to the PCC were enhanced. For the SN, all significant connections in EB patients were significantly decreased, except the intrinsic right AI connections. Within the DAN, more significant effective connections were observed to be reduced between the EB and HC groups, while only the connections from the right SPL to the left SPL and the intrinsic connection in the left SPL were significantly enhanced. Furthermore, discovery of more decreased effective connections in the EB subjects suggested that the disrupted causal interactions between specific regions are responsive to the compensatory brain plasticity in early deprivation.

Distinctive Correspondence Between Separable Visual Attention Functions and Intrinsic Brain Networks

Separable visual attention functions are assumed to rely on distinct but interacting neural mechanisms. Bundesen's “theory of visual attention” (TVA) allows the mathematical estimation of independent parameters that characterize individuals' visual attentional capacity (i.e., visual processing speed and visual short-term memory storage capacity) and selectivity functions (i.e., top-down control and spatial laterality). However, it is unclear whether these parameters distinctively map onto different brain networks obtained from intrinsic functional connectivity, which organizes slowly fluctuating ongoing brain activity. In our study, 31 demographically homogeneous healthy young participants performed whole- and partial-report tasks and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Report accuracy was modeled using TVA to estimate, individually, the four TVA parameters. Networks encompassing cortical areas relevant for visual attention were derived from independent component analysis of rs-fMRI data: visual, executive control, right and left frontoparietal, and ventral and dorsal attention networks. Two TVA parameters were mapped on particular functional networks. First, participants with higher (vs. lower) visual processing speed showed lower functional connectivity within the ventral attention network. Second, participants with more (vs. less) efficient top-down control showed higher functional connectivity within the dorsal attention network and lower functional connectivity within the visual network. Additionally, higher performance was associated with higher functional connectivity between networks: specifically, between the ventral attention and right frontoparietal networks for visual processing speed, and between the visual and executive control networks for top-down control. The higher inter-network functional connectivity was related to lower intra-network connectivity. These results demonstrate that separable visual attention parameters that are assumed to constitute relatively stable traits correspond distinctly to the functional connectivity both within and between particular functional networks. This implies that individual differences in basic attention functions are represented by differences in the coherence of slowly fluctuating brain activity.