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The ability to read minds has long been a fascination of science fiction, but revolutionary new brain-imaging methods are bringing it closer to scientific reality. The New Mind Readers provides a compelling look at the origins, development, and future of these extraordinary tools, revealing how they are increasingly being used to decode our thoughts and experiences--and how this raises sometimes troubling questions about their application in domains such as marketing, politics, and the law. Russell Poldrack takes readers on a journey of scientific discovery, telling the stories of the visionaries behind these breakthroughs. Along the way, he gives an insider's perspective on what is perhaps the single most important technology in cognitive neuroscience today--functional magnetic resonance imaging, or fMRI, which is providing astonishing new insights into the contents and workings of the mind. He highlights both the amazing power and major limitations of these techniques and describes how applications outside the lab often exceed the bounds of responsible science. Poldrack also details the unique and sometimes disorienting experience of having his own brain scanned more than a hundred times as part of a landmark study of how human brain function changes over time. Written by one of the world's leading pioneers in the field, The New Mind Readers cuts through the hype and misperceptions surrounding these emerging new methods, offering needed perspective on what they can and cannot do--and demonstrating how they can provide new answers to age-old questions about the nature of consciousness and what it means to be human. -- Inside jacket flap.

The amygdala and the hippocampus are two limbic structures that play a critical role in cognition and behavior, however their manual segmentation and that of their smaller nuclei/subfields in multicenter datasets is time consuming and difficult due to the low contrast of standard MRI. Here, we assessed the reliability of the automated segmentation of amygdalar nuclei and hippocampal subfields across sites and vendors using FreeSurfer in two independent cohorts of older and younger healthy adults. Sixty-five healthy older (cohort 1) and 68 younger subjects (cohort 2), from the PharmaCog and CoRR consortia, underwent repeated 3D-T1 MRI (interval 1–90 days). Segmentation was performed using FreeSurfer v6.0. Reliability was assessed using volume reproducibility error (ε) and spatial overlapping coefficient (DICE) between test and retest session. Significant MRI site and vendor effects (p ​< ​.05) were found in a few subfields/nuclei for the ε, while extensive effects were found for the DICE score of most subfields/nuclei. Reliability was strongly influenced by volume, as ε correlated negatively and DICE correlated positively with volume size of structures (absolute value of Spearman’s r correlations >0.43, p ​< ​1.39E-36). In particular, volumes larger than 200 ​mm3 (for amygdalar nuclei) and 300 ​mm3 (for hippocampal subfields, except for molecular layer) had the best test-retest reproducibility (ε ​< ​5% and DICE ​> ​0.80). Our results support the use of volumetric measures of larger amygdalar nuclei and hippocampal subfields in multisite MRI studies. These measures could be useful for disease tracking and assessment of efficacy in drug trials. •Differences in MRI site/vendor had a limited effect on volume reproducibility.•Differences in MRI site/vendor had an extensive effect on spatial accuracy.•Reliability is good for larger amygdalar and hippocampal structures.•Automated volumetry is reliable in multicenter MRI studies.

Memory loss is one of the first symptoms of typical Alzheimer's disease (AD), for which there are no effective therapies available. The precuneus (PC) has been recently emphasized as a key area for the memory impairment observed in early AD, likely due to disconnection mechanisms within large-scale networks such as the default mode network (DMN). Using a multimodal approach we investigated in a two-week, randomized, sham-controlled, double-blinded trial the effects of high-frequency repetitive transcranial magnetic stimulation (rTMS) of the PC on cognition, as measured by the Alzheimer Disease Cooperative Study Preclinical Alzheimer Cognitive Composite in 14 patients with early AD (7 females). TMS combined with electroencephalography (TMS-EEG) was used to detect changes in brain connectivity. We found that rTMS of the PC induced a selective improvement in episodic memory, but not in other cognitive domains. Analysis of TMS-EEG signal revealed an increase of neural activity in patients' PC, an enhancement of brain oscillations in the beta band and a modification of functional connections between the PC and medial frontal areas within the DMN. Our findings show that high-frequency rTMS of the PC is a promising, non-invasive treatment for memory dysfunction in patients at early stages of AD. This clinical improvement is accompanied by modulation of brain connectivity, consistently with the pathophysiological model of brain disconnection in AD. •The precuneus is a key area for memory impairment in Alzheimer’s disease (AD).•We investigated the effects of precuneus-rTMS on memory in patients with early AD.•Precuneus-rTMS induced a selective improvement in episodic memory.•Precuneus-rTMS enhance precuneus activity and connectivity with frontal areas.•Precuneus-rTMS is a promising treatment for memory dysfunction in early AD patients.

Meditation training can improve mood and emotion regulation, yet the neural mechanisms of these affective changes have yet to be fully elucidated. We evaluated the impact of long- and short-term mindfulness meditation training on the amygdala response to emotional pictures in a healthy, non-clinical population of adults using blood-oxygen level dependent functional magnetic resonance imaging. Long-term meditators (N = 30, 16 female) had 9081 h of lifetime practice on average, primarily in mindfulness meditation. Short-term training consisted of an 8-week Mindfulness- Based Stress Reduction course (N = 32, 22 female), which was compared to an active control condition (N = 35, 19 female) in a randomized controlled trial. Meditation training was associated with less amygdala reactivity to positive pictures relative to controls, but there were no group differences in response to negative pictures. Reductions in reactivity to negative stimuli may require more practice experience or concentrated practice, as hours of retreat practice in long-term meditators was associated with lower amygdala reactivity to negative pictures – yet we did not see this relationship for practice time with MBSR. Short-term training, compared to the control intervention, also led to increased functional connectivity between the amygdala and a region implicated in emotion regulation – ventromedial prefrontal cortex (VMPFC) – during affective pictures. Thus, meditation training may improve affective responding through reduced amygdala reactivity, and heightened amygdala–VMPFC connectivity during affective stimuli may reflect a potential mechanism by which MBSR exerts salutary effects on emotion regulation ability. •Mindfulness meditation related to lower amygdala activation to positive pictures.•Amygdala-prefrontal coupling increased after Mindfulness-Based Stress Reduction.•Amygdala activation to negative pictures was lower with more practice on retreat.

Mounting evidence suggests that function and connectivity of the striatum is disrupted in schizophrenia 1 – 5 . We have developed a new hypothesis-driven neuroimaging biomarker for schizophrenia identification, prognosis and subtyping based on functional striatal abnormalities (FSA). FSA scores provide a personalized index of striatal dysfunction, ranging from normal to highly pathological. Using inter-site cross-validation on functional magnetic resonance images acquired from seven independent scanners ( n  = 1,100), FSA distinguished individuals with schizophrenia from healthy controls with an accuracy exceeding 80% (sensitivity, 79.3%; specificity, 81.5%). In two longitudinal cohorts, inter-individual variation in baseline FSA scores was significantly associated with antipsychotic treatment response. FSA revealed a spectrum of severity in striatal dysfunction across neuropsychiatric disorders, where dysfunction was most severe in schizophrenia, milder in bipolar disorder, and indistinguishable from healthy individuals in depression, obsessive-compulsive disorder and attention-deficit hyperactivity disorder. Loci of striatal hyperactivity recapitulated the spatial distribution of dopaminergic function and the expression profiles of polygenic risk for schizophrenia. In conclusion, we have developed a new biomarker to index striatal dysfunction and established its utility in predicting antipsychotic treatment response, clinical stratification and elucidating striatal dysfunction in neuropsychiatric disorders. A new cross-validated neuroimaging biomarker that reflects striatal dysfunctioning can be used to distinguish patients with schizophrenia from healthy controls, and is associated with treatment response to antipsychotics.

The thalamus has a key role in mediating cortical–subcortical interactions but is often neglected in neuroimaging studies, which mostly focus on changes in cortical structure and activity. One of the main reasons for the thalamus being overlooked is that the delineation of individual thalamic nuclei via neuroimaging remains controversial. Indeed, neuroimaging atlases vary substantially regarding which thalamic nuclei are included and how their delineations were established. Here, we review current and emerging methods for thalamic nuclei segmentation in neuroimaging data and consider the limitations of existing techniques in terms of their research and clinical applicability. We address these challenges by proposing a roadmap to improve thalamic nuclei segmentation in human neuroimaging and, in turn, harmonize research approaches and advance clinical applications. We believe that a collective effort is required to achieve this. We hope that this will ultimately lead to the thalamic nuclei being regarded as key brain regions in their own right and not (as often currently assumed) as simply a gateway between cortical and subcortical regions.The human thalamus comprises multiple nuclei with distinct connectivity patterns and anatomical features; however, current neuroimaging approaches have a limited capacity to delinate individual thalamic nuclei. Segobin and colleagues outline the challenges that this presents to our understanding of the function of these nuclei and propose a roadmap for the future of thalamic neuroimaging.

Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagnose specific brain disorders, especially when deep learning‐based AI algorithms are applied. The successful combination of different brain imaging modalities using deep learning remains a challenging yet crucial research topic. The integration of structural and functional modalities is particularly important for the diagnosis of various brain disorders, where structural information plays a crucial role in diseases such as Alzheimer's, while functional imaging is more critical for disorders such as schizophrenia. However, the combination of functional and structural imaging modalities can provide a more comprehensive diagnosis. In this work, we present MultiViT, a novel diagnostic deep learning model that utilizes vision transformers and cross‐attention mechanisms to effectively fuse information from 3D gray matter maps derived from structural MRI with functional network connectivity matrices obtained from functional MRI using the ICA algorithm. MultiViT achieves an AUC of 0.833, outperforming both our unimodal and multimodal baselines, enabling more accurate classification and diagnosis of schizophrenia. In addition, using vision transformer's unique attentional maps in combination with cross‐attentional mechanisms and brain function information, we identify critical brain regions in 3D gray matter space associated with the characteristics of schizophrenia. Our research not only significantly improves the accuracy of AI‐based automated imaging diagnostics for schizophrenia, but also pioneers a rational and advanced data fusion approach by replacing complex, high‐dimensional fMRI information with functional network connectivity, integrating it with representative structural data from 3D gray matter images, and further providing interpretative biomarker localization in a 3D structural space. The MultiViT model combines structural and functional neuroimaging data for the prediction of schizophrenia and integrates vision transformers with cross‐attention layers in order to preserve mutual information. The pipeline generates highly interpretable cross‐attention‐based brain saliency maps and emphasizes functional network connectivity patterns related to the disorder.

It is unknown whether indoles, metabolites of tryptophan that are derived entirely from bacterial metabolism in the gut, are associated with symptoms of depression and anxiety. Serum samples (baseline, 12 weeks) were drawn from participants (n = 196) randomized to treatment with cognitive behavioral therapy (CBT), escitalopram, or duloxetine for major depressive disorder. Baseline indoxyl sulfate abundance was positively correlated with severity of psychic anxiety and total anxiety and with resting state functional connectivity to a network that processes aversive stimuli (which includes the subcallosal cingulate cortex (SCC-FC), bilateral anterior insula, right anterior midcingulate cortex, and the right premotor areas). The relation between indoxyl sulfate and psychic anxiety was mediated only through the metabolite’s effect on the SCC-FC with the premotor area. Baseline indole abundances were unrelated to post-treatment outcome measures, and changes in symptoms were not correlated with changes in indole concentrations. These results suggest that CBT and antidepressant medications relieve anxiety via mechanisms unrelated to modulation of indoles derived from gut microbiota; it remains possible that treatment-related improvement stems from their impact on other aspects of the gut microbiome. A peripheral gut microbiome-derived metabolite was associated with altered neural processing and with psychiatric symptom (anxiety) in humans, which provides further evidence that gut microbiome disruption can contribute to neuropsychiatric disorders that may require different therapeutic approaches. Given the exploratory nature of this study, findings should be replicated in confirmatory studies. Clinical trial NCT00360399 “Predictors of Antidepressant Treatment Response: The Emory CIDAR” https://clinicaltrials.gov/ct2/show/NCT00360399 .

Pooling magnetic resonance imaging (MRI) data across research studies, or utilizing shared data from imaging repositories, presents exceptional opportunities to advance and enhance reproducibility of neuroscience research. However, scanner confounds hinder pooling data collected on different scanners or across software and hardware upgrades on the same scanner, even when all acquisition protocols are harmonized. These confounds reduce power and can lead to spurious findings. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach that implements a data-driven linked independent component analysis (LICA) to identify scanner-related effects for removal from multimodal MRI to denoise scanner effects. We utilized multi-study data to test our proposed method that were collected on a single 3T scanner, pre- and post-software and major hardware upgrades and using different acquisition parameters. Our proposed denoising method shows a greater reduction of scanner-related variance compared with standard GLM confound regression or ICA-based single-modality denoising. Although we did not test it here, for combining data across different scanners, LICA should prove even better at identifying scanner effects as between-scanner variability is generally much larger than within-scanner variability. Our method has great promise for denoising scanner effects in multi-study and in large-scale multi-site studies that may be confounded by scanner differences.

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA’s first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.