Shan H. Siddiqi

Head motion systematically biases functional connectivity (FC) estimates in resting-state functional MRI (rs-fMRI). A common mitigation strategy is to censor high-motion volumes and discard high-motion runs. However, overly stringent censoring risks discarding signal alongside noise, potentially degrading FC estimates. Here, we test the efficacy of various censoring strategies on individual-specific cortical parcellations and personalized transcranial magnetic stimulation (TMS) target selection. Using precision-fMRI datasets comprising 50 individuals, we define individualized “ground-truth” references from ≥1 hour of low-motion data per participant. We then simulate 10-minute or 20-minute rs-fMRI sessions with varying motion levels from the remaining data, yielding final samples of 22 and 19 participants, respectively. Higher motion produces parcellations and TMS targets that deviate further from the ground-truth references. However, at any given motion level, lenient censoring produces higher quality parcellations and personalized TMS targets than strict censoring. The improvement is comparable to doubling scan duration from 20 to 40 minutes under strict censoring. With personalized connectome-guided TMS, a common dilemma is whether to re-scan patients with only high-motion runs. A mixed-motion session with one low-motion run and one high-motion run may often be considered usable after discarding the high-motion run and strict censoring. We find that lenient censoring of high-motion-only sessions yields TMS targets comparable to—or even better than—those derived from strictly censored mixed-motion sessions. Therefore, within the motion range and parcellation/TMS targeting frameworks evaluated here, patients may not need to be re-scanned solely because all runs exceed strict censoring criteria.

Transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS) can modulate brain circuits, and certain stimulation targets may relieve or exacerbate anxiety. The objective of this study was to localize circuit-based stimulation targets that modify anxiety by integrating causal sources of information, including lesion, TMS, and DBS datasets. The authors leveraged a series of natural experiments across multiple datasets (N=936). First, patients with incidental brain lesions (N=451) and TMS-treated individuals with incidental variability in stimulation location for depression (N=111) were analyzed. Functional connectivity of these sites was estimated using a normative connectome. Next, a TMS dataset (N=300) with a common set of stimulation coordinates but individual variability in brain connectivity was analyzed. Finally, the authors examined subthalamic DBS locations in patients with Parkinson's disease (N=74). Similar brain circuits were derived from lesions associated with greater anxiety and TMS sites that decreased anxiety (spatial r=0.68). In an independent TMS dataset (N=300), individualized TMS site connectivity to this circuit was significantly correlated with treatment-induced anxiety change. In Parkinson's disease, overlap of subthalamic DBS (N=74) sites with the anxiety circuit significantly predicted greater anxiety increases, confirming network involvement despite the circuit being derived independently of DBS. The circuit was associated significantly with trait anxiety versus state anxiety in two datasets that measured both. The peak proposed TMS target for anxiety was in the right superior frontal gyrus. This study identified a convergent anxiety circuit across lesions, TMS, and DBS. This provides a potential therapeutic target for brain stimulation and represents a generalizable approach for identifying stimulation targets across neuropsychiatric conditions.

Introduction: When depressive symptoms return after an initial response to transcranial magnetic stimulation (TMS), evidence to guide use of a second acute course is strikingly limited. We aimed t o estimate how often and how quickly patients with recurrent major depressive disorder benefit from a second course of transcranial magnetic stimulation after they previously improved with TMS. Methods: A retrospective cohort study across 20 outpatient centres included 259 patients who received ≥ 2 TMS courses between January 1, 2023 and October 1, 2025, and had clinically significant depressive symptoms at the start of course 2; 177 prior responders formed the primary cohort. Treatment used standard 10‐Hz left dorsolateral prefrontal cortex TMS. Response and remission were assessed using the PHQ‐9 and/or BDI‐II; response was defined as a ≥ 50% reduction and remission as PHQ‐9 ≤ 4 and/or BDI‐II ≤ 9. Symptom change from baseline to end of course 2 and time to first response by session were compared between courses. Results: Among past responders, 146 of 177 patients (82.5%; 95% CI, 76.1%–87.8%) responded again during course 2 and 96 of 177 (54.2%; 95% CI, 46.6%–61.7%) achieved remission. PHQ‐9 scores decreased by a median of 65.0% ( p < 0.001), and BDI‐II scores decreased by a mean of 12.0 points (95% CI, −14.0 to −10.0; p < 0.001). Among responders, the first session meeting response criteria occurred earlier during retreatment than during the initial course on both PHQ‐9 (median session, 12 vs. 16; hazard ratio = 1.40, 95% CI, 1.14–1.72; p = 0.001) and BDI‐II (13.5 vs. 22.5; hazard ratio = 1.46, 95% CI, 1.07–2.00; p = 0.018). Conclusions: Most patients who previously benefited from acute TMS improved again when symptoms recurred, and among responders, the first session meeting response criteria often occurred earlier during retreatment than during the initial course.

Brain atrophy may precede cognitive and functional impairment in Alzheimer's disease (AD), but at this "preclinical" stage, it remains unclear whether atrophy localizes to specific brain networks and whether such localization is associated with clinical outcomes. We investigated cortical thickness in 1778 cognitively unimpaired (CU) older adults with amyloid-β (Aβ) PET from the A4 (Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease) and LEARN (Longitudinal Evaluation of Amyloid Risk and Neurodegeneration) studies, with a subset (N = 445) with tau PET. We estimated the networks disrupted by each individual's cortical thinning using a large normative connectome database (N = 1000), and tested whether this preclinical AD atrophy network is associated with clinical manifestations and longitudinal cognitive (PACC) and functional (CDR) trajectories. Distinct networks connected to atrophy patterns were associated with Aβ and regional tau in CU older adults. These networks were similar to a previously published atrophy network for AD dementia (Aβ: r = 0.817, P = 0.006; tau: r = 0.712, P = 0.046). Atrophy connectivity to this preclinical AD network was associated with higher Aβ and tau, independent of total cortical atrophy, and cross-sectionally with lower cognition, greater subjective cognitive decline, and increased anxiety; longitudinally, it predicted faster cognitive and functional decline over ~5 years. After tau adjustment, the functional-decline effect was preserved while the cognitive-slope effect was largely attenuated. Atrophy in preclinical AD localizes to a network resembling AD dementia, and is independently associated with AD pathologies, clinical outcomes, and longitudinal decline. Network-level neurodegeneration is detectable and clinically informative in preclinical AD, supporting future network-based research and therapeutic development.

Functional connectivity (FC) is often used to identify personalized targets for transcranial magnetic stimulation (TMS). However, existing methods often overlook individual differences in whole-cortex network organization. Furthermore, in some personalized TMS protocols, lower stimulation intensity is used for targets closer to the scalp, which may improve patient tolerance. Here, we develop an algorithm to simultaneously optimize FC and scalp proximity for target localization. We first use the multi-session hierarchical Bayesian model (MS-HBM) to estimate high-quality individual-specific cortical networks. A tree-based algorithm is then used to select the optimal target. With essentially no parameter to tune, our framework may potentially improve generalizability across populations. We compare our approach with existing “cluster” and “cone” algorithms. In two test–retest datasets of healthy individuals from the United States and Singapore, tree-based MS-HBM reliably identifies personalized TMS targets for depression near the scalp. Tree-based MS-HBM targets compare favorably with cluster and cone targets in terms of reliability, scalp proximity, and FC to the subgenual anterior cingulate cortex (sACC) in new out-of-sample MRI sessions. To demonstrate versatility, the same algorithm identifies personalized anxiety targets without tuning any parameter. In patients with treatment-resistant depression, tree-based MS-HBM targets compare favorably with cluster and cone targets in terms of reliability, scalp proximity, and sACC FC, hypothetically reducing stimulation intensity by 15% and 5%, respectively. MS-HBM also exhibits the best (most negative) electric-field hotspot sACC FC and highest reliability in induced electric fields. Overall, tree-based MS-HBM provides a robust, generalizable framework to estimate near-scalp personalized targets across populations.

Causal modulation of sympathetic arousal could yield new therapeutic targets for sympathetic hyperactivation. We employed a natural experiment in which different brain regions were incidentally targeted using transcranial magnetic stimulation (TMS) (n=139 sites, n=14 individuals), deep brain stimulation (n=392 sites, n=58 individuals), or low-intensity focused ultrasound (n=46 sites, n=23 individuals). Using the human connectome (n=1000) as a wiring diagram, we identified a convergent brain circuit connecting focal neuromodulation sites that preferentially modified sympathetic arousal across all three modalities. In independent datasets, TMS to the sympathetic circuit reduced anxiety-related sympathetic arousal on a laboratory task (n=28) and clinical questionnaire (n=30). The sympathetic circuit aligned with lesion-derived circuits previously linked to sympathetic hyperactivation (i.e., aggression, anxiety, PTSD), but not other eight other lesion circuits. It was also more similar to a map of norepinephrine transporter distribution versus 20 other neurotransmitters. This sympathetic circuit may be a promising treatment target for sympathetic hyperactivation.

Resting-state functional connectivity is increasingly used to measure neural engagement following transcranial magnetic stimulation (TMS), but whether connectivity changes are specific to the stimulation target and clinically meaningful remains unclear. In a head-to-head trial, thirty-six patients with depression and anxiety were randomized to 30 TMS sessions at one of two circuit-based targets: a dorsolateral prefrontal target hypothesized to improve "dysphoric" symptoms, and a dorsomedial prefrontal target hypothesized to improve "anxiosomatic" symptoms. We tested whether: (1) TMS selectively engaged the targeted circuit; (2) baseline TMS site connectivity to the targeted circuit predicted circuit engagement; (3) either metric correlated with improvement on the Beck Depression and Beck Anxiety Inventories. Twenty-nine participants with pre- and post-treatment MRI were included. Target engagement was defined as change in average connectivity within each circuit and compared between target groups; associations were measured using Spearman's partial correlation, controlling for relevant covariates. While connectivity decreased within both circuits, the effects were not clearly target-specific and did not predict symptom improvement. In contrast, stronger baseline TMS site connectivity to the targeted circuit predicted greater connectivity change (r = -0.61, p < 0.001) and anxiety improvement (r_w = 0.47, p = 0.004). These effects were driven by the anxiosomatic target group. Negative findings in the dysphoric target group may reflect greater inter-individual variability in dysphoric circuit topography, as suggested by post-hoc analyses. Within the constraints of a modest sample size, results suggest that baseline TMS site connectivity may represent a more informative biomarker than TMS-induced connectivity changes, and that circuit architecture should be considered when defining targeting protocols.

Therapeutic brain stimulation is believed to target specific networks, but targeting approaches for memory remain debated. For other symptoms, neuromodulation targets have been localized by mapping connectivity of lesions and stimulation sites to specific symptoms. This approach has yielded networks for global memory, but it remains unclear whether it applies to specific types of memory. Here, we mapped connectivity of stimulation sites, lesions, and atrophy patterns associated with different memory types. We included 544 individuals across three datasets: transcranial magnetic stimulation (N=262), penetrating head trauma (N=169), and ischemic stroke (N=113). We identified a network preferentially connected to lesions and stimulation sites specifically associated with changes in visual memory. Of note, the direction of this effect was inverted depending on whether lesions or stimulation occurred at younger age or an older age, consistent with prior results. This age effect was replicated in an independent dataset of patients with preclinical Alzheimers disease (N=1240). To examine neuromodulation targets, we computed electrical field models for potential TMS sites that overlap with the networks derived from each stimulation or lesion dataset; the resulting targets intersected with established targets that demonstrated efficacy for treating memory impairment - precuneus, cortical-hippocampal network, and dorsolateral prefrontal cortex - with peak intersection at medial posterior parietal lobe, angular gyrus, and left anterior middle frontal gyrus, respectively. Future head-to-head clinical trials are needed to systematically compare these proposed neuromodulation targets against each other.

Transcranial magnetic stimulation (TMS) is effective for major depressive disorder (MDD) and anxious depression despite imprecise scalp-based targeting. Retrospective connectomics analyses suggest that targeting one brain circuit preferentially improves “dysphoric” symptoms, while targeting a different brain circuit preferentially improves “anxiosomatic” symptoms. Here, we tested this hypothesis prospectively by randomizing adults with MDD (n = 40, age 18-65) who had moderate-to-severe symptoms of depression (Beck Depression Inventory (BDI) ≥ 20) and anxiety (Beck Anxiety Inventory (BAI) ≥ 16) to a 30-treatment TMS course at the dysphoric circuit target (MNI coordinates [-32, 44, 34], close to the conventional left dorsolateral prefrontal cortex target for MDD), or at the anxiosomatic circuit target (MNI coordinates [0, 48, 46], a dorsomedial target not routinely used). As hypothesized, dysphoric circuit targeting (n = 16) improved BDI more than BAI (ratio 1.08, IQR 0.69-2.02), while anxiosomatic circuit targeting (n = 20) improved BAI more than BDI (ratio 0.70, IQR 0.01-1.01) (Wilcoxon rank-sum test p = 0.0195). Both targets improved BDI (55 vs 54%), but BAI improved significantly more with anxiosomatic circuit targeting (58 vs 36%, p = 0.0301), even when controlling for BDI (p < 0.001). Thus, TMS targeting different connectome-derived brain circuits differentially modulates anxiety that is comorbid with major depressive disorder. Future studies could target according to baseline symptom profile, a step toward precision psychiatry. Trial Registration Number NCT04604210.