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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.

Attention‐deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, characterized by symptoms of age‐inappropriate inattention, hyperactivity, and impulsivity. Apart from behavioral symptoms investigated by psychiatric methods, there is no standard biological test to diagnose ADHD. This study aimed to explore whether the radiomics features based on resting‐state functional magnetic resonance (rs‐fMRI) have more discriminative power for the diagnosis of ADHD. The rs‐fMRI of 187 subjects with ADHD and 187 healthy controls were collected from 5 sites of ADHD‐200 Consortium. A total of four preprocessed rs‐fMRI images including regional homogeneity (ReHo), amplitude of low‐frequency fluctuation (ALFF), voxel‐mirrored homotopic connectivity (VMHC) and network degree centrality (DC) were used in this study. From each of the four images, we extracted 93 radiomics features within each of 116 automated anatomical labeling brain areas, resulting in a total of 43,152 features for each subject. After dimension reduction and feature selection, 19 radiomics features were retained (5 from ALFF, 9 from ReHo, 3 from VMHC and 2 from DC). By training and optimizing a support vector machine model using the retained features of training dataset, we achieved the accuracy of 76.3% and 77.0% (areas under curve = 0.811 and 0.797) in the training and testing datasets, respectively. Our findings demonstrate that radiomics can be a novel strategy for fully utilizing rs‐fMRI information to distinguish ADHD from healthy controls. The rs‐fMRI‐based radiomics features have the potential to be neuroimaging biomarkers for ADHD. Radiomics features extracted from rs‐fMRI metrics can be used for distinguishing ADHD patients from HC with better classification performance. There was a significant correlation between radiomics features and clinical measures, thus radiomics may be potential neuroimaging biomarkers of ADHD.

Objectives Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are known as the preclinical and early stage of Alzheimer's disease (AD). The dorsal attention network (DAN) is mainly responsible for the “top‐down” attention process. However, previous studies mainly focused on single functional modality and limited structure. This study aimed to investigate the multimodal alterations of DAN in SCD and aMCI to assess their diagnostic value in preclinical and early‐stage AD. Methods Resting‐state functional magnetic resonance imaging (MRI) was carried out to measure the fractional amplitude of low‐frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC). Structural MRI was used to calculate the gray matter volume (GMV) and cortical thickness. Moreover, receiver‐operating characteristic (ROC) analysis was used to distinguish these alterations in SCD and aMCI. Results The SCD and aMCI groups showed both decreased ReHo in the right middle temporal gyrus (MTG) and decreased GMV compared to healthy controls (HCs). Especially in the SCD group, there were increased fALFF and increased ReHo in the left inferior occipital gyrus (IOG), decreased fALFF and increased FC in the left inferior parietal lobule (IPL), and reduced cortical thickness in the right inferior temporal gyrus (ITG). Furthermore, functional and structural alterations in the SCD and aMCI groups were closely related to episodic memory (EM), executive function (EF), and information processing speed (IPS). The combination of multiple indicators of DAN had a high accuracy in differentiating clinical stages. Conclusions Our current study demonstrated functional and structural alterations of DAN in SCD and aMCI, especially in the MTG, IPL, and SPL. Furthermore, cognitive performance was closely related to these significant alterations. Our study further suggested that the combined multiple indicators of DAN could be acted as the latent neuroimaging markers of preclinical and early‐stage AD for their high diagnostic value. Our current study demonstrated obviously functional and structural alterations of DAN in SCD and aMCI. We found that the abnormalities of the functional alterations were especially located in the IPL, IOG, and MTG, whereas structural alterations were mainly in the SPL and ITG. Furthermore, cognitive performance was closely related to these significant alterations. Our study further suggested that the combined multiple indicators of DAN could be acted as the latent neuroimaging markers of preclinical and early‐stage AD for their high diagnostic value.

Although previous neuroimaging evidence has confirmed the brain functional disturbances in thyroid‐associated ophthalmopathy (TAO), the dynamic characteristics of brain activity and functional connectivity (FC) in TAO were rarely concerned. The present study aims to investigate the alterations of temporal variability of brain activity and FC in TAO using resting‐state functional magnetic resonance imaging (rs‐fMRI). Forty‐seven TAO patients and 30 age‐, gender‐, education‐, and handedness‐matched healthy controls (HCs) were enrolled and underwent rs‐fMRI scanning. The dynamic amplitude of low‐frequency fluctuation (dALFF) was first calculated using a sliding window approach to characterize the temporal variability of brain activity. Based on the dALFF results, seed‐based dynamic functional connectivity (dFC) analysis was performed to identify the temporal variability of efficient communication between brain regions in TAO. Additionally, correlations between dALFF and dFC and the clinical indicators were analyzed. Compared with HCs, TAO patients displayed decreased dALFF in the left superior occipital gyrus (SOG) and cuneus (CUN), while showing increased dALFF in the left triangular part of inferior frontal gyrus (IFGtriang), insula (INS), orbital part of inferior frontal gyrus (ORBinf), superior temporal gyrus (STG) and temporal pole of superior temporal gyrus (TPOsup). Furthermore, TAO patients exhibited decreased dFC between the left STG and the right middle occipital gyrus (MOG), as well as decreased dFC between the left TPOsup and the right calcarine fissure and surrounding cortex (CAL) and MOG. Correlation analyses showed that the altered dALFF in the left SOG/CUN was positively related to visual acuity (r = .409, p = .004), as well as the score of QoL for visual functioning (r = .375, p = .009). TAO patients developed abnormal temporal variability of brain activity in areas related to vision, emotion, and cognition, as well as reduced temporal variability of FC associated with vision deficits. These findings provided additional insights into the neurobiological mechanisms of TAO. We explored the spatiotemporal alterations of both brain activity and connectivity in thyroid‐associated ophthalmopathy (TAO) by using dynamic amplitude of low‐frequency fluctuation and dynamic functional connectivity, respectively. We also identified correlations between the abnormal dynamic brain activity in the left occipital area and the visual deficits in TAO patients.

Differentiating the parkinsonian variant of multiple system atrophy (MSA‐P) from idiopathic Parkinson's disease (IPD) is challenging, especially in the early stages. This study aimed to investigate differences and similarities in the brain functional connectomes of IPD and MSA‐P patients and use machine learning methods to explore the diagnostic utility of these features. Resting‐state fMRI data were acquired from 88 healthy controls, 76 MSA‐P patients, and 53 IPD patients using a 3.0 T scanner. The whole‐brain functional connectome was constructed by thresholding the Pearson correlation matrices of 116 regions, and topological properties were evaluated through graph theory approaches. Connectome measurements were used as features in machine learning models (random forest [RF]/logistic regression [LR]/support vector machine) to distinguish IPD and MSA‐P patients. Regarding graph metrics, early IPD and MSA‐P patients shared network topological properties. Both patient groups showed functional connectivity disruptions within the cerebellum‐basal ganglia‐cortical network, but these disconnections were mainly in the cortico‐thalamo‐cerebellar circuits in MSA‐P patients and the basal ganglia‐thalamo‐cortical circuits in IPD patients. Among the connectome parameters, t tests combined with the RF method identified 15 features, from which the LR classifier achieved the best diagnostic performance on the validation set (accuracy = 92.31%, sensitivity = 90.91%, specificity = 93.33%, area under the receiver operating characteristic curve = 0.89). MSA‐P and IPD patients show similar whole‐brain network topological alterations. MSA‐P primarily affects cerebellar nodes, and IPD primarily affects basal ganglia nodes; both conditions disrupt the cerebellum‐basal ganglia‐cortical network. Moreover, functional connectome parameters showed outstanding value in the differential diagnosis of early MSA‐P and IPD. We investigated brain functional connectome abnormalities in patients with idiopathic Parkinson's disease (IPD) and parkinsonian variant of multiple system atrophy (MSA‐P) using resting‐state functional magnetic resonance imaging, demonstrating that “disconnection” within the cortico‐basal ganglia‐cerebellar network serve as the cornerstone of a differential diagnosis. IPD patients have decreased functional connectivity (FC) in the basal ganglia‐cortical circuit, whereas early‐stage MSA‐P patients have reduced FC in the cerebellum‐cortical circuit.

Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter‐cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study‐specific imaging indices. We proposed a novel framework for inter‐cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting‐state functional MRI (fMRI) between MCI converters (MCI_C) and non‐converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3‐year follow‐up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross‐validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter‐network hypo‐connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging‐based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi‐modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter‐cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future. Inter‐cohort validation was performed for MRI‐based prediction of MCI conversion. The framework integrated structural, static, and dynamic functional features. Altered dynamic functional features were reported in MCI converters.

Transcranial direct and alternating current stimulation (tDCS and tACS, respectively) entail capability to modulate human brain dynamics and cognition. However, the comparability of these approaches at the level of large-scale functional networks has not been thoroughly investigated. In this study, 44 subjects were randomly assigned to receive sham ( = 15), tDCS ( = 15), or tACS ( = 14). The first electrode (anode in tDCS) was positioned over the left dorsolateral prefrontal cortex, the target area, and the second electrode (cathode in tDCS) was placed over the right supraorbital region. tDCS was delivered with a constant current of 2 mA. tACS was fixed to 2 mA peak-to-peak with 6 Hz frequency. Stimulation was applied concurrently with functional magnetic resonance imaging (fMRI) acquisitions, both at rest and during the performance of a verbal working memory (WM) task. After stimulation, subjects repeated the fMRI WM task. Our results indicated that at rest, tDCS increased functional connectivity particularly within the default-mode network (DMN), while tACS decreased it. When comparing both fMRI WM tasks, it was observed that tDCS displayed decreased brain activity post-stimulation as compared to online. Conversely, tACS effects were driven by neural increases online as compared to post-stimulation. Interestingly, both effects primarily occurred within DMN-related areas. Regarding the differences in each fMRI WM task, during the online fMRI WM task, tACS engaged distributed neural resources which did not overlap with the WM-dependent activity pattern, but with some posterior DMN regions. In contrast, during the post-stimulation fMRI WM task, tDCS strengthened prefrontal DMN deactivations, being these activity reductions associated with faster responses. Furthermore, it was observed that tDCS neural responses presented certain consistency across distinct fMRI modalities, while tACS did not. In sum, tDCS and tACS modulate fMRI-derived network dynamics differently. However, both effects seem to focus on DMN regions and the WM network-DMN shift, which are highly affected in aging and disease. Thus, albeit exploratory and needing further replication with larger samples, our results might provide a refined understanding of how the DMN functioning can be externally modulated through commonly used non-invasive brain stimulation techniques, which may be of eventual clinical relevance.

Transcranial direct current stimulation (tDCS) over prefrontal cortex (PFC) regions is currently proposed as therapeutic intervention for major depression and other psychiatric disorders. The in-depth mechanistic understanding of this bipolar and non-focal stimulation technique is still incomplete. In a pilot study, we investigated the effects of bifrontal stimulation on brain metabolite levels and resting state connectivity under the cathode using multiparametric MRI techniques and computational tDCS modeling. Within a double-blind cross-over design, 20 subjects (12 women, 23.7 ± 2 years) were randomized to active tDCS with standard bifrontal montage with the anode over the left dorsolateral prefrontal cortex (DLPFC) and the cathode over the right DLPFC. Magnetic resonance spectroscopy (MRS) was acquired before, during, and after prefrontal tDCS to quantify glutamate (Glu), Glu + glutamine (Glx) and gamma aminobutyric acid (GABA) concentration in these areas. Resting-state functional connectivity MRI (rsfcMRI) was acquired before and after the stimulation. The individual distribution of tDCS induced electric fields (efields) within the MRS voxel was computationally modelled using SimNIBS 2.0. There were no significant changes of Glu, Glx and GABA levels across conditions but marked differences in the course of Glu levels between female and male participants were observed. Further investigation yielded a significantly stronger Glu reduction after active compared to sham stimulation in female participants, but not in male participants. For rsfcMRI neither significant changes nor correlations with MRS data were observed. Exploratory analyses of the effect of efield intensity distribution on Glu changes showed distinct effects in different efield groups. Our findings are limited by the small sample size, but correspond to previously published results of cathodal tDCS. Future studies should address gender and efield intensity as moderators of tDCS induced effects.

This study explored how the neural efficiency and proficiency worked in athletes with different skill levels from the perspective of effective connectivity brain network in resting state. The deconvolved conditioned Granger causality (GC) analysis was applied to functional magnetic resonance imaging (fMRI) data of 35 elite athletes (EAs) and 42 student‐athletes (SAs) of racket sports as well as 39 normal controls (NCs), to obtain the voxel‐wised hemodynamic response function (HRF) parameters representing the functional segregation and effective connectivity representing the functional integration. The results showed decreased time‐to‐peak of HRF in the visual attention brain regions in the two athlete groups compared with NC and decreased response height in the advanced motor control brain regions in EA comparing to the nonelite groups, suggesting the neural efficiency represented by the regional HRF was different in early and advanced skill levels. GC analysis demonstrated that the GC values within the middle occipital gyrus had a linear trend from negative to positive, suggesting a stepwise “neural proficiency” of the effective connectivity from NC to SA then to EA. The GC values of the inter‐lobe circuits in EA had the trend to regress to NC levels, in agreement with the neural efficiency of these circuits in EA. Further feature selection approach suggested the important role of the cerebral‐brainstem GC circuit for discriminating EA. Our findings gave new insight into the complementary neural mechanisms in brain functional segregation and integration, which was associated with early and advanced skill levels in athletes of racket sports. The neural efficiency represented by regional hemodynamic response function (HRF) was different in early and advanced stages of motor training. The Granger causality (GC) values within the middle occipital gyrus (MOG) had a linear trend from negative to positive, suggesting a stepwise “neural proficiency” of the effective connectivity from normal controls (NC) to student‐athletes (SA) then to elite athletes (EA). While the GC values of the fronto‐parietal and fronto‐occipital circuits in EA had the trend to regress to NC levels, in agreement with the neural efficiency of these circuits in EA. Complementary neural mechanisms in brain functional segregation and integration were associated with different levels of sports experience in fast‐ball athletes.

Numerous studies indicate altered static local and long‐range functional connectivity of multiple brain regions in schizophrenia patients with auditory verbal hallucinations (AVHs). However, the temporal dynamics of interhemispheric and intrahemispheric functional connectivity patterns remain unknown in schizophrenia patients with AVHs. We analyzed resting‐state functional magnetic resonance imaging data for drug‐naïve first‐episode schizophrenia patients, 50 with AVHs and 50 without AVH (NAVH), and 50 age‐ and sex‐matched healthy controls. Whole‐brain functional connectivity was decomposed into ipsilateral and contralateral parts, and sliding‐window analysis was used to calculate voxel‐wise interhemispheric and intrahemispheric dynamic functional connectivity density (dFCD). Finally, the correlation analysis was performed between abnormal dFCD variance and clinical measures in the AVH and NAVH groups. Compared with the NAVH group and healthy controls, the AVH group showed weaker interhemispheric dFCD variability in the left middle temporal gyrus (p < .01; p < .001), as well as stronger interhemispheric dFCD variability in the right thalamus (p < .001; p < .001) and right inferior temporal gyrus (p < .01; p < .001) and stronger intrahemispheric dFCD variability in the left inferior frontal gyrus (p < .001; p < .01). Moreover, abnormal contralateral dFCD variability of the left middle temporal gyrus correlated with the severity of AVHs in the AVH group (r = −.319, p = .024). The findings demonstrate that abnormal temporal variability of interhemispheric and intrahemispheric dFCD in schizophrenia patients with AVHs mainly focus on the temporal and frontal cortices and thalamus that are pivotal components of auditory and language pathways. Here, we collected and analyzed resting‐state functional magnetic resonance imaging data for drug‐naïve first‐episode schizophrenia patients, 50 with auditory verbal hallucinations (AVHs) and 50 without AVH (NAVH), and 50 age‐ and sex‐matched healthy controls. Whole‐brain functional connectivity was decomposed into ipsilateral and contralateral functional connectivity, and sliding‐window analysis was used to calculate the interhemispheric and intrahemispheric dynamic functional connectivity density (dFCD). The findings demonstrate that abnormal temporal variability of interhemispheric and intrahemispheric dFCD in schizophrenia patients with AVHs mainly focuses on the temporal and frontal cortices and thalamus that are pivotal components of auditory and language pathways.