Tobacco cigarette smoking is associated with disrupted brain network dynamics in resting brain networks including the Salience (SN) and Fronto parietal (FPN). Unified multimodal methods [Resting state connectivity analysis, Diffusion Tensor Imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and cortical thickness analysis] were employed to test the hypothesis that the impact of cigarette smoking on the balance among these networks is due to alterations in white matter connectivity, microstructural architecture, functional connectivity and cortical thickness (CT) and that these metrics define fundamental differences between people who smoke and nonsmokers. Multimodal analyses of previously collected 7 Tesla MRI data via the Human Connectome Project were performed on 22 people who smoke (average number of daily cigarettes was 10 ± 5) and 22 age- and sex-matched nonsmoking controls. First, functional connectivity analysis was used to examine SN-FPN-DMN interactions between people who smoke and nonsmokers. The anatomy of these networks was then assessed using DTI and CT analyses while microstructural architecture of WM was analyzed using the NODDI toolbox. Seed-based connectivity analysis revealed significantly enhanced within network [p = 0.001 FDR corrected] and between network functional coupling of the salience and R-frontoparietal networks in people who smoke [p = 0.004 FDR corrected]. The network connectivity was lateralized to the right hemisphere. Whole brain diffusion analysis revealed no significant differences between people who smoke and nonsmokers in Fractional Anisotropy, Mean diffusivity and in neurite orienting and density. There were also no significant differences in CT in the hubs of these networks. Our results demonstrate that tobacco cigarette smoking is associated with enhanced functional connectivity, but anatomy is largely intact in young adults. Whether this enhanced connectivity is pre-existing, transient or permanent is not known. The observed enhanced connectivity in resting state networks may contribute to the maintenance of smoking frequency.
Introduction: Proof-of-principle human studies suggest that transcranial direct current stimulation (tDCS) over the dorsolateral prefrontal cortex (DLPFC) may improve depression severity. This open-label multicenter study tested remotely supervised multichannel tDCS delivered at home in patients (N=35) with major depressive disorder (MDD). The primary aim was to assess the feasibility and safety of our protocol. As an exploratory aim, we evaluated therapeutic efficacy: the primary efficacy measure was the median percent change from baseline to the end of the 4-week post-treatment follow-up period in the observer-rated Montgomery-Asberg Depression Mood Rating Scale (MADRS). Methods: Participants received 37 at-home stimulation sessions (30 minutes each) of specifically designed multichannel tDCS targeting the left DLPFC administered over eight weeks (4 weeks of daily treatments plus 4 weeks of taper), with a follow-up period of 4 weeks following the final stimulation session. The stimulation montage (electrode positions and currents) was optimized by employing computational models of the electric field generated by multichannel tDCS using available structural data from a similar population (group optimization). Conducted entirely remotely, the study employed the MADRS for assessment at baseline, at weeks 4 and 8 during treatment, and at 4-week follow-up visits. Results: 34 patients (85.3% women) with a mean age of 59 years, a diagnosis of MDD according to DSM-5 criteria, and a MADRS score ≥20 at the time of study enrolment completed all study visits. At baseline, the mean time since MDD diagnosis was 24.0 (SD 19.1) months. Concerning compliance, 85% of the participants (n=29) completed the complete course of 37 stimulation sessions at home, while 97% completed at least 36 sessions. No detrimental effects were observed, including suicidal ideation and/or behavior. The study observed a median MADRS score reduction of 64.5% (48.6, 72.4) 4 weeks post-treatment (Hedge's g = -3.1). We observed a response rate (≥ 50% improvement in MADRS scores) of 72.7% (n=24) from baseline to the last visit 4 weeks post-treatment. Secondary measures reflected similar improvements. Conclusions: These results suggest that remotely supervised and supported multichannel home-based tDCS is safe and feasible, and antidepressant efficacy motivates further appropriately controlled clinical studies.
BACKGROUND: Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting. METHODS: Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier. RESULTS: Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82-0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70-0.73 AUC). CONCLUSIONS: These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.
Depressive Disorder, Major , Electroconvulsive Therapy , Humans , Electroconvulsive Therapy/methods , Depressive Disorder, Major/diagnostic imaging , Depressive Disorder, Major/therapy , Depressive Disorder, Major/pathology , Depression , Neuroimaging , Magnetic Resonance Imaging/methods , Biomarkers , Machine Learning , Treatment Outcome
Telehealth and telemedicine have encountered explosive growth since the beginning of the COVID-19 pandemic, resulting in increased access to care for patients located far from medical centers and clinics. Subspecialty clinicians in behavioral neurology & neuropsychiatry (BNNP) have implemented the use of telemedicine platforms to perform cognitive examinations that were previously office based. In this perspective article, BNNP clinicians at Massachusetts General Hospital (MGH) describe their experience performing cognitive examinations via telemedicine. The article reviews the goals, prerequisites, advantages, and potential limitations of performing a video- or telephone-based telemedicine cognitive examination. The article shares the approaches used by MGH BNNP clinicians to examine cognitive and behavioral areas, such as orientation, attention and executive functions, language, verbal learning and memory, visual learning and memory, visuospatial function, praxis, and abstract abilities, as well as to survey for neuropsychiatric symptoms and assess activities of daily living. Limitations of telemedicine-based cognitive examinations include limited access to and familiarity with telecommunication technologies on the patient side, limitations of the technology itself on the clinician side, and the limited psychometric validation of virtual assessments. Therefore, an in-person examination with a BNNP clinician or a formal in-person neuropsychological examination with a neuropsychologist may be recommended. Overall, this article emphasizes the use of standardized cognitive and behavioral assessment instruments that are either in the public domain or, if copyrighted, are nonproprietary and do not require a fee to be used by the practicing BNNP clinician.
COVID-19 , Neurology , Neuropsychiatry , Telemedicine , Humans , Hospitals, General , Pandemics , Activities of Daily Living , Massachusetts , Cognition
Introduction: Electroconvulsive therapy (ECT) and ketamine are two effective treatments for depression with similar efficacy; however, individual patient outcomes may be improved by models that predict optimal treatment assignment. Here, we adapt the Personalized Advantage Index (PAI) algorithm using machine learning to predict optimal treatment assignment between ECT and ketamine using medical record data from a large, naturalistic patient cohort. We hypothesized that patients who received a treatment predicted to be optimal would have significantly better outcomes following treatment compared to those who received a non-optimal treatment. Methods: Data on 2526 ECT and 235 mixed IV ketamine and esketamine patients from McLean Hospital was aggregated. Depressive symptoms were measured using the Quick Inventory of Depressive Symptomatology (QIDS) before and during acute treatment. Patients were matched between treatments on pretreatment QIDS, age, inpatient status, and psychotic symptoms using a 1:1 ratio yielding a sample of 470 patients (n=235 per treatment). Random forest models were trained and predicted differential patientwise minimum QIDS scores achieved during acute treatment (min-QIDS) scores for ECT and ketamine using pretreatment patient measures. Analysis of Shapley Additive exPlanations (SHAP) values identified predictors of differential outcomes between treatments. Results: Twenty-seven percent of patients with the largest PAI scores who received a treatment predicted optimal had significantly lower min-QIDS scores compared to those who received a non-optimal treatment (mean difference=1.6, t=2.38, q<0.05, Cohen's D=0.36). Analysis of SHAP values identified prescriptive pretreatment measures. Conclusions: Patients assigned to a treatment predicted to be optimal had significantly better treatment outcomes. Our model identified pretreatment patient factors captured in medical records that can provide interpretable and actionable guidelines treatment selection.
Background: The cerebellum contributes to the precise timing of non-motor and motor functions, and cerebellum abnormalities have been implicated in psychosis pathophysiology. In this study, we explored the effects of cerebellar theta burst stimulation (TBS), an efficient transcranial magnetic stimulation protocol, on temporal discrimination and self-reported mood and psychotic symptoms. Methods: We conducted a case-crossover study in which patients with psychosis (schizophrenias, schizoaffective disorders, or bipolar disorders with psychotic features) were assigned to three sessions of TBS to the cerebellar vermis: one session each of intermittent (iTBS), continuous (cTBS), and sham TBS. Of 28 enrolled patients, 26 underwent at least one TBS session, and 20 completed all three. Before and immediately following TBS, participants rated their mood and psychotic symptoms and performed a time interval discrimination task (IDT). We hypothesized that cerebellar iTBS and cTBS would modulate these measures in opposing directions, with iTBS being adaptive and cTBS maladaptive. Results: Reaction time (RT) in the IDT decreased significantly after iTBS vs. Sham (LS-mean difference = -73.3, p = 0.0001, Cohen's d = 1.62), after iTBS vs. cTBS (LS-mean difference = -137.6, p < 0.0001, d = 2.03), and after Sham vs. cTBS (LS-mean difference = -64.4, p < 0.0001, d = 1.33). We found no effect on IDT accuracy. We did not observe any effects on symptom severity after correcting for multiple comparisons. Conclusion: We observed a frequency-dependent dissociation between the effects of iTBS vs. cTBS to the cerebellar midline on the reaction time of interval discrimination in patients with psychosis. iTBS showed improved (adaptive) while cTBS led to worsening (maladaptive) speed of response. These results demonstrate behavioral target engagement in a cognitive dimension of relevance to patients with psychosis and generate testable hypotheses about the potential therapeutic role of cerebellar iTBS in this clinical population. Clinical Trial Registration: clinicaltrials.gov, identifier NCT02642029.
Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this causal depression network (CDN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis Principal Component Analysis (PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CDN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CDN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes (t = -2.35, p = 0.019). This evidence further supports that treatment interventions converge on a CDN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.
Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression and has been shown to modulate resting-state functional connectivity (RSFC) of depression-relevant neural circuits. To date, however, few studies have investigated whether individual treatment-related symptom changes are predictable from pretreatment RSFC. We use machine learning to predict dimensional changes in depressive symptoms using pretreatment patterns of RSFC. We hypothesized that changes in dimensional depressive symptoms would be predicted more accurately than scale total scores. Patients with depression (n=26) underwent pretreatment RSFC MRI. Depressive symptoms were assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17). Random forest regression (RFR) models were trained and tested to predict treatment-related symptom changes captured by the HDRS-17, HDRS-6 and three previously identified HDRS subscales: core mood/anhedonia (CMA), somatic disturbances, and insomnia. Changes along the CMA, HDRS-17, and HDRS-6 were predicted significantly above chance, with 9%, 2%, and 2% of out-of-sample outcome variance explained, respectively (all p<0.01). CMA changes were predicted more accurately than the HDRS-17 (p<0.05). Higher baseline global connectivity (GC) of default mode network (DMN) subregions and the somatomotor network (SMN) predicted poorer symptom reduction, while higher GC of the right dorsal attention (DAN) frontoparietal control (FPCN), and visual networks (VN) predicted reduced CMA symptoms. HDRS-17 and HDRS-6 changes were predicted with similar GC patterns. These results suggest that RSFC spanning the DMN, SMN, DAN, FPCN, and VN subregions predict dimensional changes with greater accuracy than syndromal changes following rTMS. These findings highlight the need to assess more granular clinical dimensions in therapeutic studies, particularly device neuromodulation studies, and echo earlier studies supporting that dimensional outcomes improve model accuracy.
Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this common causal network (CCN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis (Principal Component Analysis, PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CCN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CCN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes. This evidence further supports that treatment interventions converge on a CCN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.
LEARNING OBJECTIVES: ⢠Outline and discuss the fundamental physiologic, cellular, and molecular mechanisms of ECT to devise strategies to optimize therapeutic outcomes⢠Summarize the overview of ECT, its efficacy in treating depression, the known effects on cognition, evidence of mechanisms, and future directions. ABSTRACT: Electroconvulsive therapy (ECT) is the most effective treatment for a variety of psychiatric illnesses, including treatment-resistant depression, bipolar depression, mania, catatonia, and clozapine-resistant schizophrenia. ECT is a medical and psychiatric procedure whereby electrical current is delivered to the brain under general anesthesia to induce a generalized seizure. ECT has evolved a great deal since the 1930s. Though it has been optimized for safety and to reduce adverse effects on cognition, issues persist. There is a need to understand fundamental physiologic, cellular, and molecular mechanisms of ECT to devise strategies to optimize therapeutic outcomes. Clinical trials that set out to adjust parameters, electrode placement, adjunctive medications, and patient selection are critical steps towards the goal of improving outcomes with ECT. This narrative review provides an overview of ECT, its efficacy in treating depression, its known effects on cognition, evidence of its mechanisms, and future directions.
Bipolar Disorder , Catatonia , Electroconvulsive Therapy , Schizophrenia , Humans , Bipolar Disorder/drug therapy , Schizophrenia/drug therapy , Catatonia/therapy , Treatment Outcome
ABSTRACT: Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising alternative for the treatment of major depressive disorder (MDD), although its clinical effectiveness varies substantially. The effects of sex hormone fluctuations on cortical excitability have been identified as potential factors that can explain this variability. However, data on how sex hormone changes affect clinical response to rTMS is limited. To address this gap, we reviewed the literature examining the effects of sex hormones and hormonal treatments on transcranial magnetic stimulation (TMS) measures of cortical excitability. Results show that variations of endogenous estrogen, testosterone, and progesterone have modulatory effects on TMS-derived measures of cortical excitability. Specifically, higher levels of estrogen and testosterone were associated with greater cortical excitability, while higher progesterone was associated with lower cortical excitability. This highlights the importance of additional investigation into the effects of hormonal changes on rTMS outcomes and circuit-specific physiological variables. These results call for TMS clinicians to consider performing more frequent motor threshold (MT) assessments in patients receiving high doses of estrogen, testosterone, and progesterone in cases such as in vitro fertilization, hormone replacement therapy, and gender-affirming hormonal treatments. It may also be important to consider physiological hormonal fluctuations and their impact on depressive symptoms and the MT when treating female patients with rTMS.
Cortical Excitability , Depressive Disorder, Major , Humans , Female , Transcranial Magnetic Stimulation/methods , Depressive Disorder, Major/therapy , Progesterone , Evoked Potentials, Motor/physiology , Estrogens , Testosterone
BACKGROUND: Electroconvulsive therapy (ECT) and repetitive transcranial magnetic stimulation (rTMS) are effective neuromodulation therapies for treatment-resistant depression (TRD). While ECT is generally considered the most effective antidepressant, rTMS is less invasive, better tolerated and leads to more durable therapeutic benefits. Both interventions are established device antidepressants, but it remains unknown if they share a common mechanism of action. Here we aimed to compare the brain volumetric changes in patients with TRD after right unilateral (RUL) ECT versus left dorsolateral prefrontal cortex (lDLPFC) rTMS. METHODS: We assessed 32 patients with TRD before the first treatment session and after treatment completion using structural magnetic resonance imaging. Fifteen patients were treated with RUL ECT and seventeen patients received lDLPFC rTMS. RESULTS: Patients receiving RUL ECT, in comparison with patients treated with lDLPFC rTMS, showed a greater volumetric increase in the right striatum, pallidum, medial temporal lobe, anterior insular cortex, anterior midbrain, and subgenual anterior cingulate cortex. However, ECT- or rTMS-induced brain volumetric changes were not associated with the clinical improvement. LIMITATIONS: We evaluated a modest sample size with concurrent pharmacological treatment and without neuromodulation therapies randomization. CONCLUSIONS: Our findings suggest that despite comparable clinical outcomes, only RUL ECT is associated with structural change, while rTMS is not. We hypothesize that structural neuroplasticity and/or neuroinflammation may explain the larger structural changes observed after ECT, whereas neurophysiological plasticity may underlie the rTMS effects. More broadly, our results support the notion that there are multiple therapeutic strategies to move patients from depression to euthymia.
Electroconvulsive Therapy , Humans , Electroconvulsive Therapy/methods , Transcranial Magnetic Stimulation/methods , Depression/therapy , Gyrus Cinguli , Temporal Lobe , Treatment Outcome
For individuals with increased levels of neuroticism, experiencing criticism or receiving negative feedback has been associated with worse psychological and cognitive outcomes. Transcranial direct current stimulation (tDCS) can change cognitive processes in clinical populations. We bilaterally stimulated the posterior inferior parietal lobule (pIPL), a critical superficial node of the default model network. We investigated how baseline neuroticism modulates the impact of bilateral tDCS to pIPL on qualitative measures of memory after hearing criticism, hypothesizing that cathodal stimulation of the IPL would offer qualitative memory improvements for individuals with higher levels of neuroticism. Ninety individuals from the community were randomly assigned to receive anodal, cathodal, or sham stimulation while they were exposed to critical comments before and after stimulation. Participants then recalled the critical comments, and their linguistic responses were analyzed using Pennebaker's Linguistic Inquiry and Word Count software, a quantitative analysis software for linguistic data. Results showed that for individuals receiving cathodal tDCS, higher neuroticism scores corresponded with greater proportions of non-personal language (i.e., words such as "us," "they," or "other" instead of "I" or "me") when recalling negative feedback. For individuals with higher neuroticism, cathodal tDCS stimulation, rather than anodal or sham, of the pIPL prompted increased emotional distancing and perspective taking strategies when recalling criticism. These results further highlight the state-dependent nature of tDCS effects and the role of the IPL in interpersonal processing - a clinically meaningful outcome that current tDCS studies solely examining quantitative measures of memory (e.g., task-based accuracy or speed) do not reveal.
Transcranial Direct Current Stimulation , Humans , Emotions , Neuroticism , Parietal Lobe , Thinking , Transcranial Direct Current Stimulation/methods
This article discusses 2 possible mechanisms of action of electroconvulsive therapy.
Electroconvulsive Therapy , Humans , Neuronal Plasticity
Decision-making (DM) impairments are important predictors of cannabis initiation and continued use. In cannabis users, how decision-making abnormalities related to structural and functional connectivity in the brain are not fully understood. We employed a three-method multimodal image analysis and multivariate pattern analysis (MVPA) on high dimensional 7 tesla MRI images examining functional connectivity, white matter microstructure and gray matter volume in a group of cannabis users and non-users. Neuroimaging and cognitive analyses were performed on 92 CU and 92 age- matched NU from a total of 187 7T scans. CU were selected on the basis of their scores on the Semi-Structured Assessment for the Genetics of Alcoholism. The groups were first compared on a decision-making test and then on ICA based functional connectivity between corticocerebellar networks. An MVPA was done as a confirmatory analysis. The anatomy of these networks was then assessed using Diffusion Tensor imaging (DTI) and cortical volume analyses. Cannabis Users had significantly higher scores on the Iowa Gambling task (IGT) [Gambling task Percentage larger] and significantly lower scores on the [Gambling task reward Percentage smaller]. Left accumbens (L NAc) volume was significantly larger in cannabis users. DTI analysis between the groups yielded no significant (FWE corrected) differences. Resting state FC analysis of the left Cerebellum region 9 showed enhanced functional connectivity with the right nucleus accumbens and left pallidum and left putamen in CU. In addition, posterior cerebellum showed enhanced functional connectivity (FWE corrected) with 2 nodes of the DMN and left and right paracingulate (sub genual ACC) and the sub callosal cortex in CU. IGT percentage larger scores correlated with posterior cerebellar functional connectivity in non-user women. A multivariate pattern analysis confirmed this cerebellar hyperconnectivity in both groups. Our results demonstrate for the first time that deficits in DM observed in cannabis users are mirrored in hyper connectivity in corticocerebellar networks. Cortical volumes of some of the nodes of these networks showed increases in users. However, the underlying white matter was largely intact in CU. The observed DM deficits and hyper connectivity in resting networks may contribute to difficulties in quitting and/or facilitating relapse.
Cannabis , Diffusion Tensor Imaging , Humans , Female , Young Adult , Brain/diagnostic imaging , Decision Making
BACKGROUND: Repetitive Transcranial Magnetic Stimulation (rTMS) shows efficacy in the treatment of major depressive disorder using a standard course of 20-36 treatment sessions. However, research efforts are being made to improve overall response and remission rates. Evidence from open-label extension studies of randomized control trials suggests that extending the rTMS treatment course beyond 36 treatments may improve outcomes, however, little has been published on the benefit of extended TMS treatment courses in clinical practice. OBJECTIVE: In this retrospective naturalistic observational study, we studied response rates on continuation of rTMS following failure of the first round of 36 treatments. METHODS: From 142 patients who received conventional rTMS and 29 who underwent theta-burst stimulation (TBS) at Massachusetts General Hospital TMS clinical service, 28 non-responders (23 to rTMS and 5 to TBS) opted to continue their treatment beyond session 36. The treatment protocol allowed personalization in target, TMS protocol, as well as number of pulses and sessions as clinically indicated. Sustained response and remission using Hamilton Rating Scale for Depression, 17-items (HAMD-17) was the primary outcome. RESULTS: The average number of overall treatment sessions was 70.54 ± 16.73 for the sample. Overall, there was a 53.57% response rate and a 32.14% remission rate. Response and remission rates rose as the number of sessions increased and there did not appear to be a plateau in response over time. CONCLUSION: Our results support the idea that subpopulation of TMS patients are late responders. Continuation of TMS up to 72 treatments among those patients who do not meet response criteria by session 36 may improve overall response rates. While the number of subjects and study design limit generalization, given the fact that these patients were medication refractory and had failed initial course of TMS, the result of this study is encouraging.
Depressive Disorder, Major , Transcranial Magnetic Stimulation , Humans , Transcranial Magnetic Stimulation/methods , Depressive Disorder, Major/therapy , Depressive Disorder, Major/etiology , Depression , Retrospective Studies , Research Design , Treatment Outcome , Prefrontal Cortex/physiology
There are few studies on dementia and schizophrenia in older patients looking for structural differences. This paper aims to describe relation between cognitive performance and brain volumes in older schizophrenia patients. Twenty schizophrenic outpatients -10 without-dementia (SND), 10 with dementia (SD)- and fifteen healthy individuals -as the control group (CG)-, older than 50, were selected. Neuropsychological tests were used to examine cognitive domains. Brain volumes were calculated with magnetic resonance images. Cognitive performance was significantly better in CG than in schizophrenics. Cognitive performance was worst in SD than SND, except in semantic memory and visual attention. Hippocampal volumes showed significant differences between SD and CG, with predominance on the right side. Left thalamic volume was smaller in SD group than in SND. Structural differences were found in the hippocampus, amygdala, and thalamus; more evident in the amygdala and thalamus, which were mainly related to dementia. In conclusion, cognitive performance and structural changes allowed us to differentiate between schizophrenia patients and CG, with changes being more pronounced in SD than in SND. When comparing SND with SD, the functional alterations largely coincide, although sometimes in the opposite direction. Moreover, volume lost in the hippocampus, amygdala, and thalamus may be related to the possibility to develop dementia in schizophrenic patients.
Cognitive impairment, and working memory deficits in particular, are debilitating, treatment-resistant aspects of schizophrenia. Dysfunction of brain network hubs, putatively related to altered neurodevelopment, is thought to underlie the cognitive symptoms associated with this illness. Here, we used weighted degree, a robust graph theory metric representing the number of weighted connections to a node, to quantify centrality in cortical hubs in 29 patients with schizophrenia and 29 age- and gender-matched healthy controls and identify the critical nodes that underlie working memory performance. In both patients and controls, elevated weighted degree in the default mode network (DMN) was generally associated with poorer performance (accuracy and reaction time). Higher degree in the ventral attention network (VAN) nodes in the right superior temporal cortex was associated with better performance (accuracy) in patients. Degree in several prefrontal and parietal areas was associated with cognitive performance only in patients. In regions that are critical for sustained attention, these correlations were primarily driven by between-network connectivity in patients. Moreover, a cross-validated prediction analysis showed that a linear model using a summary degree score can be used to predict an individual's working memory accuracy (r = 0.35). Our results suggest that schizophrenia is associated with dysfunctional hubs in the cortical systems supporting internal and external cognition and highlight the importance of topological network analysis in the search of biomarkers for cognitive deficits in schizophrenia.
