Handling Multiplicity in Neuroimaging Through Bayesian Lenses with Multilevel Modeling

TMS-induced plasticity improving cognitive control in OCD I: Clinical and neuroimaging outcomes from a randomised trial

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) is an emerging treatment for obsessive-compulsive disorder (OCD). The neurobiological mechanisms of rTMS in OCD have been incompletely characterized. We compared clinical outcomes and changes in task-based brain activation following three different rTMS stimulation protocols, all combined with exposure and response prevention (ERP).

METHODS

In this three-arm proof-of-concept randomized trial, 61 treatment-refractory adult OCD patients received 16 sessions of rTMS immediately prior to ERP over 8 weeks, with task-based functional MRI (tb-fMRI) scans and clinical assessments pre- and post-treatment. Patients received either: high frequency (HF) rTMS to the left dorsolateral prefrontal cortex (DLPFC)(n=19(6M/13F)); HF rTMS to the left pre-supplementary motor area (preSMA)(n=23(10M/13F)); or control rTMS to the vertex(n=19(6M/13F)). Changes in tb-fMRI activation pre-post treatment were compared using both a Bayesian region-of-interest and a general linear model whole-brain approach.

RESULTS

Mean OCD symptom severity decreased significantly in all treatment groups (delta=-10.836, p<0.001, 95% CI[-12.504,-9.168]), with no differences between groups. Response rate in the entire sample was 57.4%. The DLPFC rTMS group showed decreased planning-related activation post-treatment that was associated with greater symptom improvement. No group-level activation changes were observed for the preSMA or vertex rTMS groups. Participants with greater symptom improvement in the preSMA group showed decreased error-related activation, and symptom improvement in the vertex group was associated with increased inhibition-related activation.

CONCLUSIONS

PreSMA and DLPFC rTMS combined with ERP led to activation decreases in targeted task networks in individuals showing greater symptom improvement, although we observed no differences in symptom reduction between groups.

Change in Striatal Functional Connectivity Networks Across Two Years Due to Stimulant Exposure in Childhood ADHD: Results from the ABCD Sample

Widely prescribed for Attention-Deficit/Hyperactivity Disorder (ADHD), stimulants (e.g., methylphenidate) have been studied for their chronic effects on the brain in prospective designs controlling dosage and adherence. While controlled approaches are essential, they do not approximate real-world stimulant exposure contexts where medication interruptions, dosage non-compliance, and polypharmacy are common. Brain changes in real-world conditions are largely unexplored. To fill this gap, we capitalized on the observational design of the Adolescent Brain Cognitive Development (ABCD) study to examine effects of stimulants on large-scale bilateral cortical networks' resting-state functional connectivity (rs-FC) with 6 striatal regions (left and right caudate, putamen, and nucleus accumbens) across two years in children with ADHD. Bayesian hierarchical regressions revealed associations between stimulant exposure and change in rs-FC of multiple striatal-cortical networks, affiliated with executive and visuo-motor control, which were not driven by general psychotropic medication. Of these connections, three were selective to stimulants versus stimulant naive: reduced rs-FC between caudate and frontoparietal network, and between putamen and frontoparietal and visual networks. Comparison with typically developing children in the ABCD sample revealed stronger rs-FC reduction in stimulant-exposed children for putamen and frontoparietal and visual networks, suggesting a normalizing effect of stimulants. 14% of stimulant-exposed children demonstrated reliable reduction in ADHD symptoms, and were distinguished by stronger rs-FC reduction between right putamen and visual network. Thus, stimulant exposure for a two-year period under real-world conditions modulated striatal-cortical functional networks broadly, had a normalizing effect on a subset of networks, and was associated with potential therapeutic effects involving visual attentional control.

Neuroimaging Findings in US Government Personnel and Their Family Members Involved in Anomalous Health Incidents

Importance

US government personnel stationed internationally have reported anomalous health incidents (AHIs), with some individuals experiencing persistent debilitating symptoms.

Objective

To assess the potential presence of magnetic resonance imaging (MRI)-detectable brain lesions in participants with AHIs, with respect to a well-matched control group.

Design, Setting, and Participants

This exploratory study was conducted at the National Institutes of Health (NIH) Clinical Center and the NIH MRI Research Facility between June 2018 and November 2022. Eighty-one participants with AHIs and 48 age- and sex-matched control participants, 29 of whom had similar employment as the AHI group, were assessed with clinical, volumetric, and functional MRI. A high-quality diffusion MRI scan and a second volumetric scan were also acquired during a different session. The structural MRI acquisition protocol was optimized to achieve high reproducibility. Forty-nine participants with AHIs had at least 1 additional imaging session approximately 6 to 12 months from the first visit.

Exposure

AHIs.

Main Outcomes and Measures

Group-level quantitative metrics obtained from multiple modalities: (1) volumetric measurement, voxel-wise and region of interest (ROI)-wise; (2) diffusion MRI-derived metrics, voxel-wise and ROI-wise; and (3) ROI-wise within-network resting-state functional connectivity using functional MRI. Exploratory data analyses used both standard, nonparametric tests and bayesian multilevel modeling.

Results

Among the 81 participants with AHIs, the mean (SD) age was 42 (9) years and 49% were female; among the 48 control participants, the mean (SD) age was 43 (11) years and 42% were female. Imaging scans were performed as early as 14 days after experiencing AHIs with a median delay period of 80 (IQR, 36-544) days. After adjustment for multiple comparisons, no significant differences between participants with AHIs and control participants were found for any MRI modality. At an unadjusted threshold (P < .05), compared with control participants, participants with AHIs had lower intranetwork connectivity in the salience networks, a larger corpus callosum, and diffusion MRI differences in the corpus callosum, superior longitudinal fasciculus, cingulum, inferior cerebellar peduncle, and amygdala. The structural MRI measurements were highly reproducible (median coefficient of variation <1% across all global volumetric ROIs and <1.5% for all white matter ROIs for diffusion metrics). Even individuals with large differences from control participants exhibited stable longitudinal results (typically, <±1% across visits), suggesting the absence of evolving lesions. The relationships between the imaging and clinical variables were weak (median Spearman ρ = 0.10). The study did not replicate the results of a previously published investigation of AHIs.

Conclusions and Relevance

In this exploratory neuroimaging study, there were no significant differences in imaging measures of brain structure or function between individuals reporting AHIs and matched control participants after adjustment for multiple comparisons.

Age of onset of obsessive-compulsive disorder differentially affects white matter microstructure

Previous diffusion MRI studies have reported mixed findings on white matter microstructure alterations in obsessive-compulsive disorder (OCD), likely due to variation in demographic and clinical characteristics, scanning methods, and underpowered samples. The OCD global study was created across five international sites to overcome these challenges by harmonizing data collection to identify consistent brain signatures of OCD that are reproducible and generalizable. Single-shell diffusion measures (e.g., fractional anisotropy), multi-shell Neurite Orientation Dispersion and Density Imaging (NODDI) and fixel-based measures, were extracted from skeletonized white matter tracts in 260 medication-free adults with OCD and 252 healthy controls. We additionally performed structural connectome analysis. We compared cases with controls and cases with early (<18) versus late (18+) OCD onset using mixed-model and Bayesian multilevel analysis. Compared with healthy controls, adult OCD individuals showed higher fiber density in the sagittal stratum (B[SE] = 0.10[0.05], P = 0.04) and credible evidence for higher fiber density in several other tracts. When comparing early (n = 145) and late-onset (n = 114) cases, converging evidence showed lower integrity of the posterior thalamic radiation -particularly radial diffusivity (B[SE] = 0.28[0.12], P = 0.03)-and lower global efficiency of the structural connectome (B[SE] = 15.3[6.6], P = 0.03) in late-onset cases. Post-hoc analyses indicated divergent direction of effects of the two OCD groups compared to healthy controls. Age of OCD onset differentially affects the integrity of thalamo-parietal/occipital tracts and the efficiency of the structural brain network. These results lend further support for the role of the thalamus and its afferent fibers and visual attentional processes in the pathophysiology of OCD.

Prediction of image interpretation cognitive ability under different mental workloads: a task-state fMRI study

Visual imaging experts play an important role in multiple fields, and studies have shown that the combination of functional magnetic resonance imaging and machine learning techniques can predict cognitive abilities, which provides a possible method for selecting individuals with excellent image interpretation skills. We recorded behavioral data and neural activity of 64 participants during image interpretation tasks under different workloads. Based on the comprehensive image interpretation ability, participants were divided into two groups. general linear model analysis showed that during image interpretation tasks, the high-ability group exhibited higher activation in middle frontal gyrus (MFG), fusiform gyrus, inferior occipital gyrus, superior parietal gyrus, inferior parietal gyrus, and insula compared to the low-ability group. The radial basis function Support Vector Machine (SVM) algorithm shows the most excellent performance in predicting participants' image interpretation abilities (Pearson correlation coefficient = 0.54, R2 = 0.31, MSE = 0.039, RMSE = 0.002). Variable importance analysis indicated that the activation features of the fusiform gyrus and MFG played an important role in predicting this ability. Our study revealed the neural basis related to image interpretation ability when exposed to different mental workloads. Additionally, our results demonstrated the efficacy of machine learning algorithms in extracting neural activation features to predict such ability.

Associations of prior concussion severity with brain microstructure using mean apparent propagator magnetic resonance imaging

Magnetic resonance imaging (MRI) diffusion studies have shown chronic microstructural tissue abnormalities in athletes with history of concussion, but with inconsistent findings. Concussions with post-traumatic amnesia (PTA) and/or loss of consciousness (LOC) have been connected to greater physiological injury. The novel mean apparent propagator (MAP) MRI is expected to be more sensitive to such tissue injury than the conventional diffusion tensor imaging. This study examined effects of prior concussion severity on microstructure with MAP-MRI. Collegiate-aged athletes (N = 111, 38 females; ≥6 months since most recent concussion, if present) completed semistructured interviews to determine the presence of prior concussion and associated injury characteristics, including PTA and LOC. MAP-MRI metrics (mean non-Gaussian diffusion [NG Mean], return-to-origin probability [RTOP], and mean square displacement [MSD]) were calculated from multi-shell diffusion data, then evaluated for associations with concussion severity through group comparisons in a primary model (athletes with/without prior concussion) and two secondary models (athletes with/without prior concussion with PTA and/or LOC, and athletes with/without prior concussion with LOC only). Bayesian multilevel modeling estimated models in regions of interest (ROI) in white matter and subcortical gray matter, separately. In gray matter, the primary model showed decreased NG Mean and RTOP in the bilateral pallidum and decreased NG Mean in the left putamen with prior concussion. In white matter, lower NG Mean with prior concussion was present in all ROI across all models and was further decreased with LOC. However, only prior concussion with LOC was associated with decreased RTOP and increased MSD across ROI. Exploratory analyses conducted separately in male and female athletes indicate associations in the primary model may differ by sex. Results suggest microstructural measures in gray matter are associated with a general history of concussion, while a severity-dependent association of prior concussion may exist in white matter.

Brain activation during an emotional task in participants with PTSD and borderline and/or cluster C personality disorders

INTRODUCTION

Although comorbidity of post-traumatic stress disorder (PTSD) with borderline personality disorder (BPD) and/or cluster C personality disorders (CPD) is common, neural correlates of this comorbidity are unknown.

METHODS

We acquired functional MRI scans during an emotional face task in participants with PTSD + CPD (n = 34), PTSD + BPD (n = 24), PTSD + BPD + CPD (n = 18) and controls (n = 30). We used ANCOVAs and Bayesian analyses on specific ROIs in a fearful vs. scrambled faces contrast. We also investigated associations with clinical measures.

RESULTS

There were no robust differences in brain activation between the groups with ANCOVAs. Transdiagnostically, we found a negative association between severity of dissociation and right insula and right dmPFC activation, and emotion regulation problems with right dmPFC activation. Bayesian analyses showed credible evidence for higher activation in all ROIs in the PTSD + BPD + CPD group compared to PTSD + BPD and PTSD + CPD.

DISCUSSION

Our Bayesian and correlation analyses support new dimensional conceptualizations of personality disorders.

Increased neural differentiation after a single session of aerobic exercise in older adults

Aging is associated with decreased cognitive function. One theory posits that this decline is in part due to multiple neural systems becoming dedifferentiated in older adults. Exercise is known to improve cognition in older adults, even after only a single session. We hypothesized that one mechanism of improvement is a redifferentiation of neural systems. We used a within-participant, cross-over design involving 2 sessions: either 30 minutes of aerobic exercise or 30 minutes of seated rest (n = 32; ages 55-81 years). Both functional Magnetic Resonance Imaging (fMRI) and Stroop performance were acquired soon after exercise and rest. We quantified neural differentiation via general heterogeneity regression. There were 3 prominent findings following the exercise. First, participants were better at reducing Stroop interference. Second, while there was greater neural differentiation within the hippocampal formation and cerebellum, there was lower neural differentiation within frontal cortices. Third, this greater neural differentiation in the cerebellum and temporal lobe was more pronounced in the older ages. These data suggest that exercise can induce greater neural differentiation in healthy aging.

TMS-induced plasticity improving cognitive control in OCD I: Clinical and neuroimaging outcomes from a randomised trial of rTMS for OCD

Background

Repetitive transcranial magnetic stimulation (rTMS) is an emerging treatment option for obsessive-compulsive disorder (OCD). The neurobiological mechanisms of rTMS in OCD have, however, been incompletely characterized. We compared clinical outcomes and changes in task-based brain activation following three different rTMS stimulation protocols, all combined with exposure and response prevention (ERP).

Methods

In this three-arm proof-of-concept randomized controlled clinical trial, 61 treatment-refractory adult OCD patients received 16 sessions of rTMS immediately prior to ERP over 8 weeks, with task-based functional MRI (tb-fMRI) scans and clinical assessments pre- and post-treatment. Patients received either: high frequency (HF) rTMS to the left dorsolateral prefrontal cortex (DLPFC) (n=19 (6M/13F)); HF rTMS to the left pre-supplementary motor area (preSMA) (n=23 (10M/13F)); or control rTMS to the vertex (n=19 (6M/13F)). Changes in tb-fMRI activation pre-post treatment were compared using both a Bayesian region-of-interest and a general linear model whole-brain approach.

Results

Mean OCD symptom severity decreased significantly in all treatment groups (delta=- 10.836, p<0.001, 95% CI [-12.504, -9.168]), with no differences between groups. Response rate in the entire sample was 57.4%. Groups receiving DLPFC or preSMA rTMS showed, respectively, a decrease in planning and error processing task-related activation after treatment that was associated with symptom improvement, while individuals in the vertex rTMS group with greater symptom improvement showed an increase in inhibition-related activation.

Conclusions

PreSMA and DLPFC rTMS combined with ERP led to significant symptom improvement related to activation decreases in targeted task networks, although we observed no differences in symptom reduction between groups. This trial was registered at clinicaltrials.gov ( NCT03667807 ).

Dopamine Transporter Availability in Early Parkinson's Disease is Dependent on Sunlight Exposure

BACKGROUND

Preliminary studies suggested seasonality of dopaminergic functioning, but it is unknown whether dopamine transporter (DAT) expression in humans is also dependent on the seasons. We, therefore, investigated seasonal and sunlight-dependent effects on DAT availability in early Parkinson's disease (PD) patients and healthy controls.

METHODS

DAT single-photon emission computed tomography scans (n = 730) were gathered from the Parkinson's Progression Marker Initiative (PPMI) database. We used global horizontal irradiance (GHI) as proxy for sun exposure/month and assessed associations between striatal DAT availability and season (autumn/winter versus spring/summer), GHI and latitude of the PPMI site.

RESULTS

In PD patients, DAT availability in the left caudate nucleus was higher in spring/summer (B [standard error (SE)] = 0.05 [0.02], P = 0.03) and positively associated with higher sun exposure (B [SE] = 0.59 [0.22] × 10-3 , P = 0.007). Latitude (in degrees north) of the PPMI site was negatively associated with DAT availability in both PD and healthy controls.

CONCLUSION

Striatal DAT availability may be influenced by daylight exposure. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Poor Insight in Obsessive-Compulsive Disorder as a Multifaceted Phenomenon: Evidence From Brain Activation During Symptom Provocation

BACKGROUND

Poor insight in obsessive-compulsive disorder (OCD) is associated with higher symptom severity, more comorbidities, and worse response to treatment. This study aimed to elucidate underlying mechanisms of poor insight in OCD by exploring its neurobiological correlates.

METHODS

Using a symptom provocation task during functional magnetic resonance imaging, we compared brain activation of patients with poor insight (n = 19; 14 female, 5 male), good/fair insight (n = 63; 31 female, 32 male), and healthy control participants (n = 42; 22 female, 20 male) using a Bayesian region-of-interest and a general linear model whole-brain approach. Insight was assessed using the Overvalued Ideas Scale.

RESULTS

Compared with patients with good/fair insight and healthy control participants, patients with OCD and poor insight showed widespread lower task-related activation in frontal areas (subgenual anterior cingulate cortex, ventromedial prefrontal cortex, dorsolateral prefrontal cortex, ventrolateral prefrontal cortex, supplementary motor area, precentral gyrus), parietal areas (posterior parietal cortex, precuneus), and the middle temporal gyrus and insula. Results were not driven by interindividual differences in OCD symptom severity, medication usage, age of disorder onset, or state distress levels.

CONCLUSIONS

During symptom provocation, patients with OCD and poor insight show altered activation in brain circuits that are involved in emotional processing, sensory processing, and cognitive control. Future research should focus on longitudinal correlates of insight and/or use tasks that probe emotional and sensory processing and cognitive control.

Highlight results, don't hide them: Enhance interpretation, reduce biases and improve reproducibility

Most neuroimaging studies display results that represent only a tiny fraction of the collected data. While it is conventional to present "only the significant results" to the reader, here we suggest that this practice has several negative consequences for both reproducibility and understanding. This practice hides away most of the results of the dataset and leads to problems of selection bias and irreproducibility, both of which have been recognized as major issues in neuroimaging studies recently. Opaque, all-or-nothing thresholding, even if well-intentioned, places undue influence on arbitrary filter values, hinders clear communication of scientific results, wastes data, is antithetical to good scientific practice, and leads to conceptual inconsistencies. It is also inconsistent with the properties of the acquired data and the underlying biology being studied. Instead of presenting only a few statistically significant locations and hiding away the remaining results, studies should "highlight" the former while also showing as much as possible of the rest. This is distinct from but complementary to utilizing data sharing repositories: the initial presentation of results has an enormous impact on the interpretation of a study. We present practical examples and extensions of this approach for voxelwise, regionwise and cross-study analyses using publicly available data that was analyzed previously by 70 teams (NARPS; Botvinik-Nezer, et al., 2020), showing that it is possible to balance the goals of displaying a full set of results with providing the reader reasonably concise and "digestible" findings. In particular, the highlighting approach sheds useful light on the kind of variability present among the NARPS teams' results, which is primarily a varied strength of agreement rather than disagreement. Using a meta-analysis built on the informative "highlighting" approach shows this relative agreement, while one using the standard "hiding" approach does not. We describe how this simple but powerful change in practice-focusing on highlighting results, rather than hiding all but the strongest ones-can help address many large concerns within the field, or at least to provide more complete information about them. We include a list of practical suggestions for results reporting to improve reproducibility, cross-study comparisons and meta-analyses.

Reduced vmPFC-insula functional connectivity in generalized anxiety disorder: a Bayesian confirmation study

Differences in the correlated activity of networked brain regions have been reported in individuals with generalized anxiety disorder (GAD) but an overreliance on null-hypothesis significance testing (NHST) limits the identification of disorder-relevant relationships. In this preregistered study, we applied both a Bayesian statistical framework and NHST to the analysis of resting-state fMRI scans from females with GAD and matched healthy comparison females. Eleven a-priori hypotheses about functional connectivity (FC) were evaluated using Bayesian (multilevel model) and frequentist (t-test) inference. Reduced FC between the ventromedial prefrontal cortex (vmPFC) and the posterior-mid insula (PMI) was confirmed by both statistical approaches and was associated with anxiety sensitivity. FC between the vmPFC-anterior insula, the amygdala-PMI, and the amygdala-dorsolateral prefrontal cortex (dlPFC) region pairs did not survive multiple comparison correction using the frequentist approach. However, the Bayesian model provided evidence for these region pairs having decreased FC in the GAD group. Leveraging Bayesian modeling, we demonstrate decreased FC of the vmPFC, insula, amygdala, and dlPFC in females with GAD. Exploiting the Bayesian framework revealed FC abnormalities between region pairs excluded by the frequentist analysis and other previously undescribed regions in GAD, demonstrating the value of applying this approach to resting-state FC data in clinical investigations.

Altered within- and between-network functional connectivity in atypical Alzheimer's disease

Posterior cortical atrophy and logopenic progressive aphasia are atypical clinical presentations of Alzheimer's disease. Resting-state functional connectivity studies have shown functional network disruptions in both phenotypes, particularly involving the language network in logopenic progressive aphasia and the visual network in posterior cortical atrophy. However, little is known about how connectivity differs both within and between brain networks in these atypical Alzheimer's disease phenotypes. A cohort of 144 patients was recruited by the Neurodegenerative Research Group at Mayo Clinic, Rochester, MN, USA, and underwent structural and resting-state functional MRI. Spatially preprocessed data were analysed to explore the default mode network and the salience, sensorimotor, language, visual and memory networks. The data were analysed at the voxel and network levels. Bayesian hierarchical linear models adjusted for age and sex were used to analyse within- and between-network connectivity. Reduced within-network connectivity was observed in the language network in both phenotypes, with stronger evidence of reductions in logopenic progressive aphasia compared to controls. Only posterior cortical atrophy showed reduced within-network connectivity in the visual network compared to controls. Both phenotypes showed reduced within-network connectivity in the default mode and sensorimotor networks. No significant change was noted in the memory network, but a slight increase in the salience within-network connectivity was seen in both phenotypes compared to controls. Between-network analysis in posterior cortical atrophy showed evidence of reduced visual-to-language network connectivity, with reduced visual-to-salience network connectivity, compared to controls. An increase in visual-to-default mode network connectivity was noted in posterior cortical atrophy compared to controls. Between-network analysis in logopenic progressive aphasia showed evidence of reduced language-to-visual network connectivity and an increase in language-to-salience network connectivity compared to controls. Findings from the voxel-level and network-level analysis were in line with the Bayesian hierarchical linear model analysis, showing reduced connectivity in the dominant network based on diagnosis and more crosstalk between networks in general compared to controls. The atypical Alzheimer's disease phenotypes were associated with disruptions in connectivity, both within and between brain networks. Phenotype-specific differences in connectivity patterns were noted in the visual network for posterior cortical atrophy and the language network for logopenic progressive aphasia.

Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition

Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based functional magnetic resonance imaging (fMRI). Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n = 3,989). We compared 9 predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and ordinary least squares (OLS) multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than nonlinear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects, and Friedman's H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.

Visual recognition in rhesus monkeys requires area TE but not TEO

The primate visual system is often described as a hierarchical feature-conjunction pathway, whereby each level represents an increasingly complex combination of image elements, culminating in the representation of whole coherent images in anterior inferior temporal cortex. Although many models of the ventral visual stream emphasize serial feedforward processing ((Poggio T, Mutch J, Leibo J, Rosasco L, Tacchetti A. The computationalmagic of the ventral stream: sketch of a theory (and why some deep architectures work). TechRep MIT-CSAIL-TR-2012-035. MIT CSAIL, Cambridge, MA. 2012); (Yamins DLK, DiCarlo JJ. Eight open questions in the computational modeling of higher sensory cortex. Curr Opin Neurobiol. 2016:37:114-120.)), anatomical studies show connections that bypass intermediate areas and that feedback to preceding areas ((Distler C, Boussaoud D, Desimone R, Ungerleider LG. Cortical connections of inferior temporal area TEO in macaque monkeys. J Comp Neurol. 1993:334(1):125-150.); (Kravitz DJ, Saleem KS, Baker CI, Mishkin M. A new neural framework for visuospatial processing. Nat Rev Neurosci. 2011:12(4):217-230.)). Prior studies on visual discrimination and object transforms also provide evidence against a strictly feed-forward serial transfer of information between adjacent areas ((Kikuchi R, Iwai E. The locus of the posterior subdivision of the inferotemporal visual learning area in the monkey. Brain Res. 1980:198(2):347-360.); (Weiskrantz L, Saunders RC. Impairments of visual object transforms in monkeys. Brain. 1984:107(4):1033-1072.); (Kar K, DiCarlo JJ. Fast recurrent processing via ventrolateral prefrontal cortex is needed by the primate ventral stream for robust Core visual object recognition. Neuron. 2021:109(1):164-176.e5.)). Thus, we sought to investigate whether behaviorally relevant propagation of visual information is as strictly sequential as sometimes supposed. We compared the accuracy of visual recognition after selective removal of specific subregions of inferior temporal cortex-area TEO, area TE, or both areas combined. Removal of TEO alone had no detectable effect on recognition memory, whereas removal of TE alone produced a large and significant impairment. Combined removal of both areas created no additional deficit relative to removal of TE alone. Thus, area TE is critical for rapid visual object recognition, and detailed image-level visual information can reach area TE via a route other than through TEO.

Longitudinal volumetric evaluation of hippocampus and amygdala subregions in recent trauma survivors

The hippocampus and the amygdala play a central role in post-traumatic stress disorder (PTSD) pathogenesis. While alternations in volumes of both regions have been consistently observed in individuals with PTSD, it remains unknown whether these reflect pre-trauma vulnerability traits or acquired post-trauma consequences of the disorder. Here, we conducted a longitudinal panel study of adult civilian trauma survivors admitted to a general hospital emergency department (ED). One hundred eligible participants (mean age = 32.97 ± 10.97, n = 56 females) completed both clinical interviews and structural MRI scans at 1-, 6-, and 14-months after ED admission (alias T1, T2, and T3). While all participants met PTSD diagnosis at T1, only n = 29 still met PTSD diagnosis at T3 (a "non-Remission" Group), while n = 71 did not (a "Remission" Group). Bayesian multilevel modeling analysis showed robust evidence for smaller right hippocampus volume (P+ of ~0.014) and moderate evidence for larger left amygdala volume (P+ of ~0.870) at T1 in the "non-Remission" group, compared to the "Remission" group. Subregion analysis further demonstrated robust evidence for smaller volume in the subiculum and right CA1 hippocampal subregions (P+ of ~0.021-0.046) in the "non-Remission" group. No time-dependent volumetric changes (T1 to T2 to T3) were observed across all participants or between groups. Results support the "vulnerability trait" hypothesis, suggesting that lower initial volumes of specific hippocampus subregions are associated with non-remitting PTSD. The stable volume of all hippocampal and amygdala subregions does not support the idea of consequential, progressive, stress-related atrophy during the first critical year following trauma exposure.

Layer-specific, retinotopically-diffuse modulation in human visual cortex in response to viewing emotionally expressive faces

Viewing faces that are perceived as emotionally expressive evokes enhanced neural responses in multiple brain regions, a phenomenon thought to depend critically on the amygdala. This emotion-related modulation is evident even in primary visual cortex (V1), providing a potential neural substrate by which emotionally salient stimuli can affect perception. How does emotional valence information, computed in the amygdala, reach V1? Here we use high-resolution functional MRI to investigate the layer profile and retinotopic distribution of neural activity specific to emotional facial expressions. Across three experiments, human participants viewed centrally presented face stimuli varying in emotional expression and performed a gender judgment task. We found that facial valence sensitivity was evident only in superficial cortical layers and was not restricted to the retinotopic location of the stimuli, consistent with diffuse feedback-like projections from the amygdala. Together, our results provide a feedback mechanism by which the amygdala directly modulates activity at the earliest stage of visual processing.

Hierarchical Bayesian modeling of the relationship between task-related hemodynamic responses and cortical excitability

Investigating the relationship between task-related hemodynamic responses and cortical excitability is challenging because it requires simultaneous measurement of hemodynamic responses while applying noninvasive brain stimulation. Moreover, cortical excitability and task-related hemodynamic responses are both associated with inter-/intra-subject variability. To reliably assess such a relationship, we applied hierarchical Bayesian modeling. This study involved 16 healthy subjects who underwent simultaneous Paired Associative Stimulation (PAS10, PAS25, Sham) while monitoring brain activity using functional Near-Infrared Spectroscopy (fNIRS), targeting the primary motor cortex (M1). Cortical excitability was measured by Motor Evoked Potentials (MEPs), and the motor task-related hemodynamic responses were measured using fNIRS 3D reconstructions. We constructed three models to investigate: (1) PAS effects on the M1 excitability, (2) PAS effects on fNIRS hemodynamic responses to a finger tapping task, and (3) the correlation between PAS effects on M1 excitability and PAS effects on task-related hemodynamic responses. Significant increase in cortical excitability was found following PAS25, whereas a small reduction of the cortical excitability was shown after PAS10 and a subtle increase occurred after sham. Both HbO and HbR absolute amplitudes increased after PAS25 and decreased after PAS10. The probability of the positive correlation between modulation of cortical excitability and hemodynamic activity was 0.77 for HbO and 0.79 for HbR. We demonstrated that PAS stimulation modulates task-related cortical hemodynamic responses in addition to M1 excitability. Moreover, the positive correlation between PAS modulations of excitability and hemodynamics brought insight into understanding the fundamental properties of cortical function and cortical excitability.

Emotion dynamics as hierarchical Bayesian inference in time

What fundamental property of our environment would be most valuable and optimal in characterizing the emotional dynamics we experience in daily life? Empirical work has shown that an accurate estimation of uncertainty is necessary for our optimal perception, learning, and decision-making. However, the role of this uncertainty in governing our affective dynamics remains unexplored. Using Bayesian encoding, decoding and computational modeling, on a large-scale neuroimaging and behavioral data on a passive movie-watching task, we showed that emotions naturally arise due to ongoing uncertainty estimations about future outcomes in a hierarchical neural architecture. Several prefrontal subregions hierarchically encoded a lower-dimensional signal that highly correlated with the evolving uncertainty. Crucially, the lateral orbitofrontal cortex (lOFC) tracked the temporal fluctuations of this uncertainty and was predictive of the participants' predisposition to anxiety. Furthermore, we observed a distinct functional double-dissociation within OFC with increased connectivity between medial OFC and DMN, while with that of lOFC and FPN in response to the evolving affect. Finally, we uncovered a temporally predictive code updating an individual's beliefs spontaneously with fluctuating outcome uncertainty in the lOFC. A biologically relevant and computationally crucial parameter in the theories of brain function, we propose uncertainty to be central to the definition of complex emotions.

The neurocomputational bases of explore-exploit decision-making

Flexible decision-making requires animals to forego immediate rewards (exploitation) and try novel choice options (exploration) to discover if they are preferable to familiar alternatives. Using the same task and a partially observable Markov decision process (POMDP) model to quantify the value of choices, we first determined that the computational basis for managing explore-exploit tradeoffs is conserved across monkeys and humans. We then used fMRI to identify where in the human brain the immediate value of exploitative choices and relative uncertainty about the value of exploratory choices were encoded. Consistent with prior neurophysiological evidence in monkeys, we observed divergent encoding of reward value and uncertainty in prefrontal and parietal regions, including frontopolar cortex, and parallel encoding of these computations in motivational regions including the amygdala, ventral striatum, and orbitofrontal cortex. These results clarify the interplay between prefrontal and motivational circuits that supports adaptive explore-exploit decisions in humans and nonhuman primates.

Joint-label fusion brain atlases for dementia research in Down syndrome

Research suggests a link between Alzheimer's Disease in Down Syndrome (DS) and the overproduction of amyloid plaques. Using Positron Emission Tomography (PET) we can assess the in-vivo regional amyloid load using several available ligands. To measure amyloid distributions in specific brain regions, a brain atlas is used. A popular method of creating a brain atlas is to segment a participant's structural Magnetic Resonance Imaging (MRI) scan. Acquiring an MRI is often challenging in intellectually-imparied populations because of contraindications or data exclusion due to significant motion artifacts or incomplete sequences related to general discomfort. When an MRI cannot be acquired, it is typically replaced with a standardized brain atlas derived from neurotypical populations (i.e. healthy individuals without DS) which may be inappropriate for use in DS. In this project, we create a series of disease and diagnosis-specific (cognitively stable (CS-DS), mild cognitive impairment (MCI-DS), and dementia (DEM-DS)) probabilistic group atlases of participants with DS and evaluate their accuracy of quantifying regional amyloid load compared to the individually-based MRI segmentations. Further, we compare the diagnostic-specific atlases with a probabilistic atlas constructed from similar-aged cognitively-stable neurotypical participants. We hypothesized that regional PET signals will best match the individually-based MRI segmentations by using DS group atlases that aligns with a participant's disorder and disease status (e.g. DS and MCI-DS). Our results vary by brain region but generally show that using a disorder-specific atlas in DS better matches the individually-based MRI segmentations than using an atlas constructed from cognitively-stable neurotypical participants. We found no additional benefit of using diagnose-specific atlases matching disease status. All atlases are made publicly available for the research community.

Highlight

Down syndrome (DS) joint-label-fusion atlases provide accurate positron emission tomography (PET) amyloid measurements.A disorder-specific DS atlas is better than a neurotypical atlas for PET quantification.It is not necessary to use a disease-state-specific atlas for quantification in aged DS.Dorsal striatum results vary, possibly due to this region and dementia progression.

Reliability of task-evoked neural activation during face-emotion paradigms: Effects of scanner and psychological processes

Assessing and improving test-retest reliability is critical to efforts to address concerns about replicability of task-based functional magnetic resonance imaging. The current study uses two statistical approaches to examine how scanner and task-related factors influence reliability of neural response to face-emotion viewing. Forty healthy adult participants completed two face-emotion paradigms at up to three scanning sessions across two scanners of the same build over approximately 2 months. We examined reliability across the main task contrasts using Bayesian linear mixed-effects models performed voxel-wise across the brain. We also used a novel Bayesian hierarchical model across a predefined whole-brain parcellation scheme and subcortical anatomical regions. Scanner differences accounted for minimal variance in temporal signal-to-noise ratio and task contrast maps. Regions activated during task at the group level showed higher reliability relative to regions not activated significantly at the group level. Greater reliability was found for contrasts involving conditions with clearly distinct visual stimuli and associated cognitive demands (e.g., face vs. nonface discrimination) compared to conditions with more similar demands (e.g., angry vs. happy face discrimination). Voxel-wise reliability estimates tended to be higher than those based on predefined anatomical regions. This work informs attempts to improve reliability in the context of task activation patterns and specific task contrasts. Our study provides a new method to estimate reliability across a large number of regions of interest and can inform researchers' selection of task conditions and analytic contrasts.

Testing the adolescent social reorientation model during self and other evaluation using hierarchical growth curve modeling with parcellated fMRI data

Adolescence is characterized as a period when relationships and experiences shift toward peers. The social reorientation model of adolescence posits this shift is driven by neurobiological changes that increase the salience of social information related to peer integration and acceptance. Although influential, this model has rarely been subjected to tests that could falsify it, or studied in longitudinal samples assessing within-person development. We focused on two phenomena that are highly salient and dynamic during adolescence—social status and self-perception—and examined longitudinal changes in neural responses during a self/other evaluation task. We expected status-related social information to uniquely increase across adolescence in social brain regions. Despite using hierarchical growth curve modeling with parcellated whole-brain data to increase power to detect developmental effects, we didn’t find evidence in support of this hypothesis. Social brain regions showed increased responsivity across adolescence, but this trajectory was not unique to status-related information. Additionally, brain regions associated with self-focused cognition showed heightened responses during self-evaluation in the transition to mid-adolescence, especially for status-related information. These results qualify existing models of adolescent social reorientation and highlight the multifaceted changes in self and social development that could be leveraged in novel ways to support adolescent health and well-being.

Neural correlates of working memory and compensation at different stages of cognitive impairment in Parkinson’s disease

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PD patients have two types of compensatory mechanisms for working memory (WM).

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First, hyperactivation for different WM load tasks depending on cognitive status.

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PD-CogNL has hyperactivation for moderate and heavy working memory load tasks.

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PD-MCI and PDD have hyperactivation for control and light working memory load tasks.

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Second, bilateral recruitment of WM-related areas improves WM performance.

Working memory (WM) impairment is one of the most frequent cognitive deficits in Parkinson's disease (PD). However, it is not known how neural activity is altered and compensatory responses eventually fail during progression. We aimed to elucidate neural correlates of WM and compensatory mechanisms in PD. Eighteen cognitively normal PD patients (PD-CogNL), 16 with PD with mild cognitive impairment (PD-MCI), 11 with PD with dementia (PDD), and 17 healthy controls (HCs) were evaluated. Subjects performed an n-back task. Functional MRI data were analyzed by event-related analysis for correct responses. Brain activations were evaluated by comparing them to fixation cross or 0-back task, and correlated with n-back task performance. When compared to fixation cross, PD-CogNL patients had more activation in WM areas than HCs for both the 2- and 3-back tasks. PD-MCI and PDD patients had more activation in WM areas than HCs for the 0- and 1-back task. 2-back task performance was correlated with brain activations (vs. 0-back task) in the bilateral dorsolateral prefrontal cortex and frontal eye field (FEF) and left rostral prefrontal cortex, caudate nucleus, inferior/superior parietal lobule (IPL/SPL), and anterior insular cortex as well as anterior cingulate cortex. 3-back task performance was correlated with brain activations (vs. 0-back task) in the left FEF, right caudate nucleus, and bilateral IPL/SPL. Additional activations on top of the 0-back task, rather than fixation cross, are the neural correlates of WM. Our results suggest PD patients have two types of compensatory mechanisms: (1) Hyperactivation for different WM load tasks depending on their cognitive status. PD-CogNL have hyperactivation for moderate and heavy working memory load tasks while maintaining normal working memory performance. In contrast, PD-MCI and PDD have hyperactivation for control task and light working memory load task, leaving less neural resources to further activate for more demanding tasks and resulting in impaired working memory performance. (2) Bilateral recruitment of WM-related areas, in particular the DLPFC, FEF, IPL/SPL and caudate nucleus, to improve WM performance.

Distributed and Multifaceted Effects of Threat and Safety

In the present fMRI study, we examined how anxious apprehension is processed in the human brain. A central goal of the study was to test the prediction that a subset of brain regions would exhibit sustained response profiles during threat periods, including the anterior insula, a region implicated in anxiety disorders. A second important goal was to evaluate the responses in the amygdala and the bed nucleus of the stria terminals, regions that have been suggested to be involved in more transient and sustained threat, respectively. A total of 109 participants performed an experiment in which they encountered "threat" or "safe" trials lasting approximately 16 sec. During the former, they experienced zero to three highly unpleasant electrical stimulations, whereas in the latter, they experienced zero to three benign electrical stimulations (not perceived as unpleasant). The timing of the stimulation during trials was randomized, and as some trials contained no stimulation, stimulation delivery was uncertain. We contrasted responses during threat and safe trials that did not contain electrical stimulation, but only the potential that unpleasant (threat) or benign (safe) stimulation could occur. We employed Bayesian multilevel analysis to contrast responses to threat and safe trials in 85 brain regions implicated in threat processing. Our results revealed that the effect of anxious apprehension is distributed across the brain and that the temporal evolution of the responses is quite varied, including more transient and more sustained profiles, as well as signal increases and decreases with threat.

Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference

Classical null hypothesis significance testing is limited to the rejection of the point-null hypothesis; it does not allow the interpretation of non-significant results. This leads to a bias against the null hypothesis. Herein, we discuss statistical approaches to ‘null effect’ assessment focusing on the Bayesian parameter inference (BPI). Although Bayesian methods have been theoretically elaborated and implemented in common neuroimaging software packages, they are not widely used for ‘null effect’ assessment. BPI considers the posterior probability of finding the effect within or outside the region of practical equivalence to the null value. It can be used to find both ‘activated/deactivated’ and ‘not activated’ voxels or to indicate that the obtained data are not sufficient using a single decision rule. It also allows to evaluate the data as the sample size increases and decide to stop the experiment if the obtained data are sufficient to make a confident inference. To demonstrate the advantages of using BPI for fMRI data group analysis, we compare it with classical null hypothesis significance testing on empirical data. We also use simulated data to show how BPI performs under different effect sizes, noise levels, noise distributions and sample sizes. Finally, we consider the problem of defining the region of practical equivalence for BPI and discuss possible applications of BPI in fMRI studies. To facilitate ‘null effect’ assessment for fMRI practitioners, we provide Statistical Parametric Mapping 12 based toolbox for Bayesian inference.

Theory-driven classification of reading difficulties from fMRI data using Bayesian latent-mixture models

Decades of research have led to several competing theories regarding the neural contributors to impaired reading. But how can we know which theory (or theories) identifies the types of markers that indeed differentiate between individuals with reading disabilities (RD) and their typically developing (TD) peers? To answer this question, we propose a new analytical tool for theory evaluation and comparison, grounded in the Bayesian latent-mixture modeling framework. We start by constructing a series of latent-mixture classification models, each reflecting one existing theoretical claim regarding the neurofunctional markers of RD (highlighting network-level differences in either mean activation, inter-subject heterogeneity, inter-region variability, or connectivity). Then, we run each model on fMRI data alone (i.e., while models are blind to participants' behavioral status), which enables us to interpret the fit between a model's classification of participants and their behavioral (known) RD/TD status as an estimate of its explanatory power. Results from n=127 adolescents and young adults (RD: n=59; TD: n=68) show that models based on network-level differences in mean activation and heterogeneity failed to differentiate between TD and RD individuals. In contrast, classifications based on variability and connectivity were significantly associated with participants' behavioral status. These findings suggest that differences in inter-region variability and connectivity may be better network-level markers of RD than mean activation or heterogeneity (at least in some populations and tasks). More broadly, the results demonstrate the promise of latent-mixture modeling as a theory-driven tool for evaluating different theoretical claims regarding neural contributors to language disorders and other cognitive traits.

Bayes estimate of primary threshold in clusterwise functional magnetic resonance imaging inferences

Clusterwise statistical inference is the most widely used technique for functional magnetic resonance imaging (fMRI) data analyses. Clusterwise statistical inference consists of two steps: (i) primary thresholding that excludes less significant voxels by a prespecified cut-off (eg, p < . 001 ); and (ii) clusterwise thresholding that controls the familywise error rate caused by clusters consisting of false positive suprathreshold voxels. The selection of the primary threshold is critical because it determines both statistical power and false discovery rate (FDR). However, in most existing statistical packages, the primary threshold is selected based on prior knowledge (eg, p < . 001 ) without taking into account the information in the data. In this article, we propose a data-driven approach to algorithmically select the optimal primary threshold based on an empirical Bayes framework. We evaluate the proposed model using extensive simulation studies and real fMRI data. In the simulation, we show that our method can effectively increase statistical power by 20% to over 100% while effectively controlling the FDR. We then investigate the brain response to the dose-effect of chlorpromazine in patients with schizophrenia by analyzing fMRI scans and generate consistent results.

Into the Unknown: Examining Neural Representations of Parent-Adolescent Interactions

The parent-adolescent relationship is important for adolescents' emotion regulation (ER), yet little is known regarding the neural patterns of dyadic ER that occur during parent-adolescent interactions. A novel measure that can be used to examine such patterns is cross-brain connectivity (CBC)-concurrent and time-lagged connectivity between two individuals' brain regions. This study sought to provide evidence of CBC and explore associations between CBC, parenting, and adolescent internalizing symptoms. Thirty-five adolescents (mean age = 15 years, 69% female, 72% Non-Hispanic White, 17% Black, 11% Hispanic or Latino) and one biological parent (94% female) completed an fMRI hyperscanning conflict discussion task. Results revealed CBC between emotion-related brain regions. Exploratory analyses indicated CBC is associated with parenting and adolescent depressive symptoms.

Beyond linearity in neuroimaging: Capturing nonlinear relationships with application to longitudinal studies

The ubiquitous adoption of linearity for quantitative predictors in statistical modeling is likely attributable to its advantages of straightforward interpretation and computational feasibility. The linearity assumption may be a reasonable approximation especially when the variable is confined within a narrow range, but it can be problematic when the variable's effect is non-monotonic or complex. Furthermore, visualization and model assessment of a linear fit are usually omitted because of challenges at the whole brain level in neuroimaging. By adopting a principle of learning from the data in the presence of uncertainty to resolve the problematic aspects of conventional polynomial fitting, we introduce a flexible and adaptive approach of multilevel smoothing splines (MSS) to capture any nonlinearity of a quantitative predictor for population-level neuroimaging data analysis. With no prior knowledge regarding the underlying relationship other than a parsimonious assumption about the extent of smoothness (e.g., no sharp corners), we express the unknown relationship with a sufficient number of smoothing splines and use the data to adaptively determine the specifics of the nonlinearity. In addition to introducing the theoretical framework of MSS as an efficient approach with a counterbalance between flexibility and stability, we strive to (a) lay out the specific schemes for population-level nonlinear analyses that may involve task (e.g., contrasting conditions) and subject-grouping (e.g., patients vs controls) factors; (b) provide modeling accommodations to adaptively reveal, estimate and compare any nonlinear effects of a predictor across the brain, or to more accurately account for the effects (including nonlinear effects) of a quantitative confound; (c) offer the associated program 3dMSS to the neuroimaging community for whole-brain voxel-wise analysis as part of the AFNI suite; and (d) demonstrate the modeling approach and visualization processes with a longitudinal dataset of structural MRI scans.

Improving the replicability of neuroimaging findings by thresholding effect sizes instead of p-values

The classical approach for testing statistical images using spatial extent inference (SEI) thresholds the statistical image based on the p‐value. This approach has an unfortunate consequence on the replicability of neuroimaging findings because the targeted brain regions are affected by the sample size—larger studies have more power to detect smaller effects. Here, we use simulations based on the preprocessed Autism Brain Imaging Data Exchange (ABIDE) to show that thresholding statistical images by effect sizes has more consistent estimates of activated regions across studies than thresholding by p‐values. Using a constant effect size threshold means that the p‐value threshold naturally scales with the sample size to ensure that the target set is similar across repetitions of the study that use different sample sizes. As a consequence of thresholding by the effect size, the type 1 and type 2 error rates go to zero as the sample size gets larger. We use a newly proposed robust effect size index that is defined for an arbitrary statistical image so that effect size thresholding can be used regardless of the test statistic or model.

To pool or not to pool: Can we ignore cross-trial variability in FMRI?

In this work, we investigate the importance of explicitly accounting for cross-trial variability in neuroimaging data analysis. To attempt to obtain reliable estimates in a task-based experiment, each condition is usually repeated across many trials. The investigator may be interested in (a) condition-level effects, (b) trial-level effects, or (c) the association of trial-level effects with the corresponding behavior data. The typical strategy for condition-level modeling is to create one regressor per condition at the subject level with the underlying assumption that responses do not change across trials. In this methodology of complete pooling, all cross-trial variability is ignored and dismissed as random noise that is swept under the rug of model residuals. Unfortunately, this framework invalidates the generalizability from the confine of specific trials (e.g., particular faces) to the associated stimulus category ("face"), and may inflate the statistical evidence when the trial sample size is not large enough. Here we propose an adaptive and computationally tractable framework that meshes well with the current two-level pipeline and explicitly accounts for trial-by-trial variability. The trial-level effects are first estimated per subject through no pooling. To allow generalizing beyond the particular stimulus set employed, the cross-trial variability is modeled at the population level through partial pooling in a multilevel model, which permits accurate effect estimation and characterization. Alternatively, trial-level estimates can be used to investigate, for example, brain-behavior associations or correlations between brain regions. Furthermore, our approach allows appropriate accounting for serial correlation, handling outliers, adapting to data skew, and capturing nonlinear brain-behavior relationships. By applying a Bayesian multilevel model framework at the level of regions of interest to an experimental dataset, we show how multiple testing can be addressed and full results reported without arbitrary dichotomization. Our approach revealed important differences compared to the conventional method at the condition level, including how the latter can distort effect magnitude and precision. Notably, in some cases our approach led to increased statistical sensitivity. In summary, our proposed framework provides an effective strategy to capture trial-by-trial responses that should be of interest to a wide community of experimentalists.

Controllability over stressor decreases responses in key threat-related brain areas

Controllability over stressors has major impacts on brain and behavior. In humans, however, the effect of controllability on responses to stressors is poorly understood. Using functional magnetic resonance imaging (fMRI), we investigated how controllability altered responses to a shock-plus-sound stressor with a between-group yoked design, where participants in controllable and uncontrollable groups experienced matched stressor exposure. Employing Bayesian multilevel analysis at the level of regions of interest and voxels in the insula, and standard voxelwise analysis, we found that controllability decreased stressor-related responses across threat-related regions, notably in the bed nucleus of the stria terminalis and anterior insula. Posterior cingulate cortex, posterior insula, and possibly medial frontal gyrus showed increased responses during control over stressor. Our findings support the idea that the aversiveness of stressors is reduced when controllable, leading to decreased responses across key regions involved in anxiety-related processing, even at the level of the extended amygdala.

Improving practices and inferences in developmental cognitive neuroscience

The past decade has seen growing concern about research practices in cognitive neuroscience, and psychology more broadly, that shake our confidence in many inferences in these fields. We consider how these issues affect developmental cognitive neuroscience, with the goal of progressing our field to support strong and defensible inferences from our neurobiological data. This manuscript focuses on the importance of distinguishing between confirmatory versus exploratory data analysis approaches in developmental cognitive neuroscience. Regarding confirmatory research, we discuss problems with analytic flexibility, appropriately instantiating hypotheses, and controlling the error rate given how we threshold data and correct for multiple comparisons. To counterbalance these concerns with confirmatory analyses, we present two complementary strategies. First, we discuss the advantages of working within an exploratory analysis framework, including estimating and reporting effect sizes, using parcellations, and conducting specification curve analyses. Second, we summarize defensible approaches for null hypothesis significance testing in confirmatory analyses, focusing on transparent and reproducible practices in our field. Specific recommendations are given, and templates, scripts, or other resources are hyperlinked, whenever possible.

Untangling the relatedness among correlations, part III: Inter-subject correlation analysis through Bayesian multilevel modeling for naturalistic scanning

While inter-subject correlation (ISC) analysis is a powerful tool for naturalistic scanning data, drawing appropriate statistical inferences is difficult due to the daunting task of accounting for the intricate relatedness in data structure as well as handling the multiple testing issue. Although the linear mixed-effects (LME) modeling approach (Chen et al., 2017a) is capable of capturing the relatedness in the data and incorporating explanatory variables, there are a few challenging issues: 1) it is difficult to assign accurate degrees of freedom for each testing statistic, 2) multiple testing correction is potentially over-penalizing due to model inefficiency, and 3) thresholding necessitates arbitrary dichotomous decisions. Here we propose a Bayesian multilevel (BML) framework for ISC data analysis that integrates all regions of interest into one model. By loosely constraining the regions through a weakly informative prior, BML dissolves multiplicity through conservatively pooling the effect of each region toward the center and improves collective fitting and overall model performance. In addition to potentially achieving a higher inference efficiency, BML improves spatial specificity and easily allows the investigator to adopt a philosophy of full results reporting. A dataset of naturalistic scanning is utilized to illustrate the modeling approach with 268 parcels and to showcase the modeling capability, flexibility and advantages in results reporting. The associated program will be available as part of the AFNI suite for general use.

Cluster failure or power failure? Evaluating sensitivity in cluster-level inference

Pioneering work in human neuroscience has relied on the ability to map brain function using task-based fMRI, but the empirical validity of these inferential methods is still being characterized. A recent landmark study by Eklund and colleagues showed that popular multiple comparison corrections based on cluster extent suffer from unexpectedly low specificity (i.e., high false positive rate). Yet that study's focus on specificity, while important, is incomplete. The validity of a method depends also on its sensitivity (i.e., true positive rate or power), yet the sensitivity of cluster correction remains poorly understood. Here, we assessed the sensitivity of gold-standard nonparametric cluster correction by resampling real data from five tasks in the Human Connectome Project and comparing results with those from the full "ground truth" datasets (n ​= ​480-493). Critically, we found that sensitivity after correction is lower than may be practical for many fMRI applications. In particular, sensitivity to medium-sized effects (|Cohen's d| ​= ​0.5) was less than 20% across tasks on average, about three times smaller than without any correction. Furthermore, cluster extent correction exhibited a spatial bias in sensitivity that was independent of effect size. In comparison, correction based on the Threshold-Free Cluster Enhancement (TFCE) statistic approximately doubled sensitivity across tasks but increased spatial bias. These results suggest that we have, until now, only measured the tip of the iceberg in the activation-mapping literature due to our goal of limiting the familywise error rate through cluster extent-based inference. There is a need to revise our practices to improve sensitivity; we therefore conclude with a list of modern strategies to boost sensitivity while maintaining respectable specificity in future investigations.

Interactions between emotion and action in the brain

A growing literature supports the existence of interactions between emotion and action in the brain, and the central participation of the anterior midcingulate cortex (aMCC) in this regard. In the present functional magnetic resonance imaging study, we sought to investigate the role of self-relevance during such interactions by varying the context in which threating pictures were presented (with guns pointed towards or away from the observer). Participants performed a simple visual detection task following exposure to such stimuli. Except for voxelwise tests, we adopted a Bayesian analysis framework which evaluated evidence for the hypotheses of interest, given the data, in a continuous fashion. Behaviorally, our results demonstrated a valence by context interaction such that there was a tendency of speeding up responses to targets after viewing threat pictures directed towards the participant. In the brain, interaction patterns that paralleled those observed behaviorally were observed most notably in the middle temporal gyrus, supplementary motor area, precentral gyrus, and anterior insula. In these regions, activity was overall greater during threat conditions relative to neutral ones, and this effect was enhanced in the directed towards context. A valence by context interaction was observed in the aMCC too, where we also observed a correlation (across participants) of evoked responses and reaction time data. Taken together, our study revealed the context-sensitive engagement of motor-related areas during emotional perception, thus supporting the idea that emotion and action interact in important ways in the brain.

Brain function and clinical characterization in the Boston adolescent neuroimaging of depression and anxiety study

We present a Human Connectome Project study tailored toward adolescent anxiety and depression. This study is one of the first studies of the Connectomes Related to Human Diseases initiative and is collecting structural, functional, and diffusion-weighted brain imaging data from up to 225 adolescents (ages 14-17 years), 150 of whom are expected to have a current diagnosis of an anxiety and/or depressive disorder. Comprehensive clinical and neuropsychological evaluations and longitudinal clinical data are also being collected. This article provides an overview of task functional magnetic resonance imaging (fMRI) protocols and preliminary findings (N = 140), as well as clinical and neuropsychological characterization of adolescents. Data collection is ongoing for an additional 85 adolescents, most of whom are expected to have a diagnosis of an anxiety and/or depressive disorder. Data from the first 140 adolescents are projected for public release through the National Institutes of Health Data Archive (NDA) with the timing of this manuscript. All other data will be made publicly-available through the NDA at regularly scheduled intervals. This article is intended to serve as an introduction to this project as well as a reference for those seeking to clinical, neurocognitive, and task fMRI data from this public resource.

Fighting or embracing multiplicity in neuroimaging? neighborhood leverage versus global calibration

Neuroimaging faces the daunting challenge of multiple testing - an instance of multiplicity - that is associated with two other issues to some extent: low inference efficiency and poor reproducibility. Typically, the same statistical model is applied to each spatial unit independently in the approach of massively univariate modeling. In dealing with multiplicity, the general strategy employed in the field is the same regardless of the specifics: trust the local "unbiased" effect estimates while adjusting the extent of statistical evidence at the global level. However, in this approach, modeling efficiency is compromised because each spatial unit (e.g., voxel, region, matrix element) is treated as an isolated and independent entity during massively univariate modeling. In addition, the required step of multiple testing "correction" by taking into consideration spatial relatedness, or neighborhood leverage, can only partly recoup statistical efficiency, resulting in potentially excessive penalization as well as arbitrariness due to thresholding procedures. Moreover, the assigned statistical evidence at the global level heavily relies on the data space (whole brain or a small volume). The present paper reviews how Stein's paradox (1956) motivates a Bayesian multilevel (BML) approach that, rather than fighting multiplicity, embraces it to our advantage through a global calibration process among spatial units. Global calibration is accomplished via a Gaussian distribution for the cross-region effects whose properties are not a priori specified, but a posteriori determined by the data at hand through the BML model. Our framework therefore incorporates multiplicity as integral to the modeling structure, not a separate correction step. By turning multiplicity into a strength, we aim to achieve five goals: 1) improve the model efficiency with a higher predictive accuracy, 2) control the errors of incorrect magnitude and incorrect sign, 3) validate each model relative to competing candidates, 4) reduce the reliance and sensitivity on the choice of data space, and 5) encourage full results reporting. Our modeling proposal reverberates with recent proposals to eliminate the dichotomization of statistical evidence ("significant" vs. "non-significant"), to improve the interpretability of study findings, as well as to promote reporting the full gamut of results (not only "significant" ones), thereby enhancing research transparency and reproducibility.

Neural correlates of symptom provocation in pediatric obsessive-compulsive disorder

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Largest controlled pediatric OCD symptom provocation study.

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Novel standardized symptom provocation picture set for pediatric OCD.

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Behavioral group differences strongest for ‘Just Right’ factor.

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Temporal pole recruited by OCD group.

Obsessive-compulsive disorder

(OCD)-affected adults and children exhibit three to four symptom dimensions with distinct but overlapping neural correlates. No symptom provocation behavioural or imaging study has examined all symptom dimensions in a pediatric OCD sample.

Method

Clinically diagnosed pediatric OCD-affected participants (n = 25) as well as age, gender and Tanner pubertal stage-matched healthy controls (HCs; n = 24) (total sample: mean age = 14.77 ± 2.93 years; age range = 9–18 years; 35% male) viewed alternating blocks of OCD symptom provocation (Contamination, Bad Thoughts, and Just Right symptom dimensions), Fear, Neutral and Rest (i.e. fixation) conditions during functional magnetic resonance imaging. A region-of-interest analysis used seeds based upon results of an adult OCD meta-analysis

Results

OCD participants found OCD symptom-related stimuli bothersome, particularly when compared to controls in the “Just Right” symptom dimension. Pediatric OCD patients exhibited greater recruitment of the left superior temporal gyrus (STG) than healthy controls during combined symptom provocation versus neutral conditions.

Conclusion

Findings suggest involvement of the temporal poles rather than in classic cortico-striatal-thalamico-cortical circuits in pediatric OCD during symptom provocation.

Inflammation and decreased functional connectivity in a widely-distributed network in depression: Centralized effects in the ventral medial prefrontal cortex

Major depressive disorder is a heterogeneous disease involving widespread disruptions in functional brain networks, the neurobiological mechanisms of which are poorly understood. Amassing evidence supports innate immune activation as one pathophysiologic mechanism contributing to depression in a subgroup of patients with elevated inflammatory markers. Although inflammation is known to alter monoamine and glutamate neurotransmitters, little work has been done to understand its role in network dysfunction in patients with depression. Here we conducted a large-scale network-based analyses of resting-state functional magnetic resonance imaging (rfMRI) data acquired from depressed patients with varying levels of inflammation to develop a comprehensive characterization of network alterations as an effect of inflammation. Complementary approaches of global brain connectivity and parcellation-based network analysis applied to the whole brain revealed that increased plasma C-reactive protein (CRP) was associated with reduced functional connectivity in a widely-distributed network including ventral striatum, parahippocampal gyrus/amygdala, orbitofrontal and insular cortices, and posterior cingulate cortex. These broad alterations were centralized in the ventral medial prefrontal cortex (vmPFC), representing a hub for the effects of inflammation on network function in the whole brain. When feeding the identified multivariate network features into a machine learning algorithm of support vector regression, we achieved high prediction accuracies for depressive symptoms that have been associated with inflammation in previous studies including anhedonia and motor slowing. These findings extend and broaden previous observations from hypothesis-driven studies, providing further support for inflammation as a distinct contributing factor to network dysfunction and symptom severity in depression.

Towards Algorithmic Analytics for Large-scale Datasets

The traditional goals of quantitative analytics cherish simple, transparent models to generate explainable insights. Large-scale data acquisition, enabled for instance by brain scanning and genomic profiling with microarray-type techniques, has prompted a wave of statistical inventions and innovative applications. Modern analysis approaches 1) tame large variable arrays capitalizing on regularization and dimensionality-reduction strategies, 2) are increasingly backed up by empirical model validations rather than justified by mathematical proofs, 3) will compare against and build on open data and consortium repositories, as well as 4) often embrace more elaborate, less interpretable models in order to maximize prediction accuracy. Here we review these trends in learning from "big data" and illustrate examples from imaging neuroscience.

An integrative Bayesian approach to matrix-based analysis in neuroimaging

Understanding the correlation structure associated with brain regions is a central goal in neuroscience, as it informs about interregional relationships and network organization. Correlation structure can be conveniently captured in a matrix that indicates the relationships among brain regions, which could involve electroencephalogram sensors, electrophysiology recordings, calcium imaging data, or functional magnetic resonance imaging (FMRI) data-We call this type of analysis matrix-based analysis, or MBA. Although different methods have been developed to summarize such matrices across subjects, including univariate general linear models (GLMs), the available modeling strategies tend to disregard the interrelationships among the regions, leading to "inefficient" statistical inference. Here, we develop a Bayesian multilevel (BML) modeling framework that simultaneously integrates the analyses of all regions, region pairs (RPs), and subjects. In this approach, the intricate relationships across regions as well as across RPs are quantitatively characterized. The adoption of the Bayesian framework allows us to achieve three goals: (a) dissolve the multiple testing issue typically associated with seeking evidence for the effect of each RP under the conventional univariate GLM; (b) make inferences on effects that would be treated as "random" under the conventional linear mixed-effects framework; and (c) estimate the effect of each brain region in a manner that indexes their relative "importance". We demonstrate the BML methodology with an FMRI dataset involving a cognitive-emotional task and compare it to the conventional GLM approach in terms of model efficiency, performance, and inferences. The associated program MBA is available as part of the AFNI suite for general use.

A tail of two sides: Artificially doubled false positive rates in neuroimaging due to the sidedness choice with t-tests

One-sided t-tests are widely used in neuroimaging data analysis. While such a test may be applicable when investigating specific regions and prior information about directionality is present, we argue here that it is often mis-applied, with severe consequences for false positive rate (FPR) control. Conceptually, a pair of one-sided t-tests conducted in tandem (e.g., to test separately for both positive and negative effects), effectively amounts to a two-sided t-test. However, replacing the two-sided test with a pair of one-sided tests without multiple comparisons correction essentially doubles the intended FPR of statements made about the same study; that is, the actual family-wise error (FWE) of results at the whole brain level would be 10% instead of the 5% intended by the researcher. Therefore, we strongly recommend that, unless otherwise explicitly justified, two-sided t-tests be applied instead of two simultaneous one-sided t-tests.