Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data.

Brain morphology varies across the ageing trajectory and the prediction of a person's age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such "brain age prediction" vary widely in terms of their methods and type of data, so at present the most accurate and generalisable methodological approach is unclear. Therefore, we used the UK Biobank data set (N = 10,824, age range 47-73) to compare the performance of the machine learning models support vector regression, relevance vector regression and Gaussian process regression on whole-brain region-based or voxel-based structural magnetic resonance imaging data with or without dimensionality reduction through principal component analysis. Performance was assessed in the validation set through cross-validation as well as an independent test set. The models achieved mean absolute errors between 3.7 and 4.7 years, with those trained on voxel-level data with principal component analysis performing best. Overall, we observed little difference in performance between models trained on the same data type, indicating that the type of input data had greater impact on performance than model choice. All code is provided online in the hope that this will aid future research.

Individual versus task differences in slow potential generators.

Average slow potentials (SPs) can be computed from any voluntary task, minimally involving attention to anticipated stimuli. Their topography when recorded by large electrode arrays even during simple tasks is complex, multifocal, and its generators appear to be equally multifocal and highly variable across subjects. Various sources of noise of course contaminate such averages and must contribute to the topographic complexity. Here, we report a study in which the non-averaged SP band (0 to 1 Hz) was analyzed by independent components (ICA), from 256 channel recordings of 18 subjects, during four task conditions (resting, visual attention, CPT, and Stroop). We intended to verify whether the replicable SP generators (between two separate day sessions) modeled as current density reconstruction on structural MRI sets were individual-specific, and if putative task-related differences were systematic across subjects. Typically, 3 ICA components (out of 10) explained SPs in each task and subject, and their combined generators were highly variable across subjects: although some occipito-temporal and medial temporal areas contained generators in most subjects; the overall patterns were obviously variable, with no single area common to all 18 subjects. Linear regression modeling to compare combined generators (from all ICA components) between tasks and sessions showed significantly higher correlations between the four tasks than between sessions for each task. Moreover, it was clear that no common task-specific areas could be seen across subjects. Those results represent one more instance in which individual case analyses favor the hypothesis of individual-specific patterns of cortical activity, regardless of task conditions. We discuss this hypothesis with respect to results from the beta band, from individual-case fMRI studies, and its corroboration by functional neurosurgery and the neuropsychology of focal lesions.

Transitions between human functional brain networks reveal complex, cost-efficient and behaviorally-relevant temporal paths.

Resting-state functional MRI activity is organized as a complex network. However, this coordinated brain activity changes with time, raising questions about its evolving temporal arrangement. Does the brain visit different configurations through time in a random or ordered way? Advances in this area depend on developing novel paradigms that would allow us to shed light on these issues. We here propose to study the temporal changes in the functional connectome by looking at transition graphs of network activity. Nodes of these graphs correspond to brief whole-brain connectivity patterns (or meta-states), and directed links to the temporal transition between consecutive meta-states. We applied this method to two datasets of healthy subjects (160 subjects and a replication sample of 54), and found that transition networks had several non-trivial properties, such as a heavy-tailed degree distribution, high clustering, and a modular organization. This organization was implemented at a low biological cost with a high cost-efficiency of the dynamics. Furthermore, characteristics of the subjects' transition graphs, including global efficiency, local efficiency and their transition cost, were correlated with cognition and motor functioning. All these results were replicated in both datasets. We conclude that time-varying functional connectivity patterns of the brain in health progress in time in a highly organized and complex order, which is related to behavior.

Are serum brain-derived neurotrophic factor concentrations related to brain structure and psychopathology in late childhood and early adolescence?

OBJECTIVE: Mental disorders can have a major impact on brain development. Peripheral blood concentrations of brain-derived neurotrophic factor (BDNF) are lower in adult psychiatric disorders. Serum BDNF concentrations and BDNF genotype have been associated with cortical maturation in children and adolescents. In 2 large independent samples, this study tests associations between serum BDNF concentrations, brain structure, and psychopathology, and the effects of BDNF genotype on BDNF serum concentrations in late childhood and early adolescence. METHODS: Children and adolescents (7-14 years old) from 2 cities (n = 267 in Porto Alegre; n = 273 in São Paulo) were evaluated as part of the Brazilian high-risk cohort (HRC) study. Serum BDNF concentrations were quantified by sandwich ELISA. Genotyping was conducted from blood or saliva samples using the SNParray Infinium HumanCore Array BeadChip. Subcortical volumes and cortical thickness were quantified using FreeSurfer. The Development and Well-Being Behavior Assessment was used to identify the presence of a psychiatric disorder. RESULTS: Serum BDNF concentrations were not associated with subcortical volumes or with cortical thickness. Serum BDNF concentration did not differ between participants with and without mental disorders, or between Val homozygotes and Met carriers. CONCLUSIONS: No evidence was found to support serum BDNF concentrations as a useful marker of developmental differences in brain and behavior in early life. Negative findings were replicated in 2 of the largest independent samples investigated to date.

Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study.

Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a "black box" that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain-based disorders which aim to overcome these limitations. We used an artificial neural network known as "deep autoencoder" to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets (n = 263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group (p < .005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations.

Network supporting contextual fear learning after dorsal hippocampal damage has increased dependence on retrosplenial cortex.

Hippocampal damage results in profound retrograde, but no anterograde amnesia in contextual fear conditioning (CFC). Although the content learned in the latter have been discussed, alternative regions supporting CFC learning were seldom proposed and never empirically addressed. Here, we employed network analysis of pCREB expression quantified from brain slices of rats with dorsal hippocampal lesion (dHPC) after undergoing CFC session. Using inter-regional correlations of pCREB-positive nuclei between brain regions, we modelled functional networks using different thresholds. The dHPC network showed small-world topology, equivalent to SHAM (control) network. However, diverging hubs were identified in each network. In a direct comparison, hubs in both networks showed consistently higher centrality values compared to the other network. Further, the distribution of correlation coefficients was different between the groups, with most significantly stronger correlation coefficients belonging to the SHAM network. These results suggest that dHPC network engaged in CFC learning is partially different, and engage alternative hubs. We next tested if pre-training lesions of dHPC and one of the new dHPC network hubs (perirhinal, Per; or disgranular retrosplenial, RSC, cortices) would impair CFC. Only dHPC-RSC, but not dHPC-Per, impaired CFC. Interestingly, only RSC showed a consistently higher centrality in the dHPC network, suggesting that the increased centrality reflects an increased functional dependence on RSC. Our results provide evidence that, without hippocampus, the RSC, an anatomically central region in the medial temporal lobe memory system might support CFC learning and memory.

Caudate volume differences among treatment responders, non-responders and controls in children with obsessive-compulsive disorder.

Treatment response in obsessive-compulsive disorder (OCD) is heterogeneous and the neurobiological underpinnings of such variability are unknown. To investigate this issue, we looked for differences in brain structures possibly associated with treatment response in children with OCD. 29 children with OCD (7-17 years) and 28 age-matched controls underwent structural magnetic resonance imaging. Patients then received treatment with fluoxetine or group cognitive-behavioral therapy during 14 weeks, and were classified as treatment responders or non-responders. The caudate nucleus, thalamus and orbitofrontal cortex were selected a priori, according to previous evidence of their association with OCD and its treatment. Gray matter (GM) volume comparisons between responders, non-responders and controls were performed, controlling for total GM volume. 17 patients were classified as responders. Differences among responders, non-responders and controls were found in both caudate nuclei (both p-values = 0.041), but after Bonferroni correction for multiple comparisons, these findings were non-significant. However, after excluding the effect of an outlier, findings were significant for the right caudate (p = 0.004). Pairwise comparisons showed larger caudate GM volume in responders versus non-responders and controls, bilaterally. The right caudate accounted for 20.2% of the variance in Y-BOCS changes after treatment in a linear regression model, with a positive correlation (p = 0.016). We present a possible neural substrate for treatment response in pediatric OCD, which is in line with previous evidence regarding the caudate nucleus. Considering the limitations, further research is needed to replicate this finding and elucidate the heterogeneity of treatment response in children with OCD (National Clinical Trials Registration Number: NCT01148316).

Cortical thickness in obsessive-compulsive disorder: multisite mega-analysis of 780 brain scans from six centres.

BACKGROUND: There is accumulating evidence for the role of fronto-striatal and associated circuits in obsessive-compulsive disorder (OCD) but limited and conflicting data on alterations in cortical thickness. AIMS: To investigate alterations in cortical thickness and subcortical volume in OCD. METHOD: In total, 412 patients with OCD and 368 healthy adults underwent magnetic resonance imaging scans. Between-group analysis of covariance of cortical thickness and subcortical volumes was performed and regression analyses undertaken. RESULTS: Significantly decreased cortical thickness was found in the OCD group compared with controls in the superior and inferior frontal, precentral, posterior cingulate, middle temporal, inferior parietal and precuneus gyri. There was also a group × age interaction in the parietal cortex, with increased thinning with age in the OCD group relative to controls. CONCLUSIONS: Our findings are partially consistent with earlier work, suggesting that group differences in grey matter volume and cortical thickness could relate to the same underlying pathology of OCD. They partially support a frontostriatal model of OCD, but also suggest that limbic, temporal and parietal regions play a role in the pathophysiology of the disorder. The group × age interaction effects may be the result of altered neuroplasticity.

Substantia nigra fractional anisotropy is not a diagnostic biomarker of Parkinson's disease: A diagnostic performance study and meta-analysis.

OBJECTIVES: Our goal was to estimate the diagnostic accuracy of substantia nigra fractional anisotropy (SN-FA) for Parkinson's disease (PD) diagnosis in a sample similar to the clinical setting, including patients with essential tremor (ET) and healthy controls (HC). We also performed a systematic review and meta-analysis to estimate mean change in SN-FA induced by PD and its diagnostic accuracy. METHODS: Our sample consisted of 135 subjects: 72 PD, 21 ET and 42 HC. To address inter-scanner variability, two 3.0-T MRI scans were performed. MRI results of this sample were pooled into a meta-analysis that included 1,432 subjects (806 PD and 626 HC). A bivariate model was used to evaluate diagnostic accuracy measures. RESULTS: In our sample, we did not observe a significant effect of disease on SN-FA and it was uninformative for diagnosis. The results of the meta-analysis estimated a 0.03 decrease in mean SN-FA in PD relative to HC (CI: 0.01-0.05). However, the discriminatory capability of SN-FA to diagnose PD was low: pooled sensitivity and specificity were 72 % (CI: 68-75) and 63 % (CI: 58-70), respectively. There was high heterogeneity between studies (I2 = 91.9 %). CONCLUSIONS: SN-FA cannot be used as an isolated measure to diagnose PD. KEY POINTS: • SN-FA appears insufficiently sensitive and specific to diagnose PD. • Radiologists must be careful when translating mean group results to clinical practice. • Imaging protocol and analysis standardization is necessary for developing reproducible quantitative biomarkers.

Flow of essential elements in subcellular fractions during oxidative stress.

Essential trace elements are commonly found in altered concentrations in the brains of patients with neurodegenerative diseases. Many studies in trace metal determination and quantification are conducted in tissue, cell culture or whole brain. In the present investigation, we determined by ICP-MS Fe, Cu, Zn, Ca, Se, Co, Cr, Mg, and Mn in organelles (mitochondria, nuclei) and whole motor neuron cell cultured in vitro. We performed experiments using two ways to access oxidative stress: cell treatments with H2O2 or Aß-42 peptide in its oligomeric form. Both treatments caused accumulation of markers of oxidative stress, such as oxidized proteins and lipids, and alteration in DNA. Regarding trace elements, cells treated with H2O2 showed higher levels of Zn and lower levels of Ca in nuclei when compared to control cells with no oxidative treatments. On the other hand, cells treated with Aß-42 peptide in its oligomeric form showed higher levels of Mg, Ca, Fe and Zn in nuclei when compared to control cells. These differences showed that metal flux in cell organelles during an intrinsic external oxidative condition (H2O2 treatment) are different from an intrinsic external neurodegenerative treatment.

Structural covariance of neostriatal and limbic regions in patients with obsessive-compulsive disorder.

BACKGROUND: Frontostriatal and frontoamygdalar connectivity alterations in patients with obsessive-compulsive disorder (OCD) have been typically described in functional neuroimaging studies. However, structural covariance, or volumetric correlations across distant brain regions, also provides network-level information. Altered structural covariance has been described in patients with different psychiatric disorders, including OCD, but to our knowledge, alterations within frontostriatal and frontoamygdalar circuits have not been explored. METHODS: We performed a mega-analysis pooling structural MRI scans from the Obsessive-compulsive Brain Imaging Consortium and assessed whole-brain voxel-wise structural covariance of 4 striatal regions (dorsal and ventral caudate nucleus, and dorsal-caudal and ventral-rostral putamen) and 2 amygdalar nuclei (basolateral and centromedial-superficial). Images were preprocessed with the standard pipeline of voxel-based morphometry studies using Statistical Parametric Mapping software. RESULTS: Our analyses involved 329 patients with OCD and 316 healthy controls. Patients showed increased structural covariance between the left ventral-rostral putamen and the left inferior frontal gyrus/frontal operculum region. This finding had a significant interaction with age; the association held only in the subgroup of older participants. Patients with OCD also showed increased structural covariance between the right centromedial-superficial amygdala and the ventromedial prefrontal cortex. LIMITATIONS: This was a cross-sectional study. Because this is a multisite data set analysis, participant recruitment and image acquisition were performed in different centres. Most patients were taking medication, and treatment protocols differed across centres. CONCLUSION: Our results provide evidence for structural network-level alterations in patients with OCD involving 2 frontosubcortical circuits of relevance for the disorder and indicate that structural covariance contributes to fully characterizing brain alterations in patients with psychiatric disorders.

Brain structural correlates of sensory phenomena in patients with obsessive-compulsive disorder.

BACKGROUND: Sensory phenomena (SP) are uncomfortable feelings, including bodily sensations, sense of inner tension, "just-right" perceptions, feelings of incompleteness, or "urge-only" phenomena, which have been described to precede, trigger or accompany repetitive behaviours in individuals with obsessive-compulsive disorder (OCD). Sensory phenomena are also observed in individuals with tic disorders, and previous research suggests that sensorimotor cortex abnormalities underpin the presence of SP in such patients. However, to our knowledge, no studies have assessed the neural correlates of SP in patients with OCD. METHODS: We assessed the presence of SP using the University of São Paulo Sensory Phenomena Scale in patients with OCD and healthy controls from specialized units in São Paulo, Brazil, and Barcelona, Spain. All participants underwent a structural magnetic resonance examination, and brain images were examined using DARTEL voxel-based morphometry. We evaluated grey matter volume differences between patients with and without SP and healthy controls within the sensorimotor and premotor cortices. RESULTS: We included 106 patients with OCD and 87 controls in our study. Patients with SP (67% of the sample) showed grey matter volume increases in the left sensorimotor cortex in comparison to patients without SP and bilateral sensorimotor cortex grey matter volume increases in comparison to controls. No differences were observed between patients without SP and controls. LIMITATIONS: Most patients were medicated. Participant recruitment and image acquisition were performed in 2 different centres. CONCLUSION: We have identified a structural correlate of SP in patients with OCD involving grey matter volume increases within the sensorimotor cortex; this finding is in agreement with those of tic disorder studies showing that abnormal activity and volume increases within this region are associated with the urges preceding tic onset.

ORBITOFRONTAL THICKNESS AS A MEASURE FOR TREATMENT RESPONSE PREDICTION IN OBSESSIVE-COMPULSIVE DISORDER.

BACKGROUND: Early prediction of treatment response could reduce exposure to ineffective treatments and optimize the use of medical resources. Neuroimaging techniques have been used to identify biomarkers that are predictive of outcomes. The aims of this study were to investigate orbitofrontal cortex (OFC) thickness as a potential morphometric biomarker to discriminate outcomes in obsessive-compulsive disorder (OCD) and then to reexamine this biomarker in an independent cohort METHODS: Using a logistic regression model based on the mean baseline thickness of subregions of the OFC, we estimated the probability of treatment response in 29 treatment-naïve OCD patients who participated in a clinical trial. That algorithm was then tested in an independent cohort of 12 patients with a confirmed diagnosis of refractory OCD RESULTS: Among the treatment-naïve OCD patients, measures of OFC thickness statistically significantly differentiated responders (n = 13) and nonresponders (n = 16), with an overall classification accuracy of ≈80%, a sensitivity of 77% (10/13), and a specificity of 81% (13/16). Of the refractory OCD patients in the second independent cohort, 67% were correctly classified as nonresponders. The most discriminative measures in the initial cohort of treatment-naïve patients were the thicknesses of the left and right medial OFC (P = .009 and P = .028, respectively) CONCLUSIONS: We found OFC thickness to be a strong predictor of treatment response in treatment-naïve OCD patients. Although there are not yet any brain imaging biomarkers with clinical utility, our results highlight the potential of these measures as tools for predicting treatment outcomes in OCD.

Comparing methods for determining motor-hand lateralization based on fTCD signals.

The lateralization index (LI) as determined from functional transcranial Doppler sonography (fTCD) can be used to determine the hemispheric organization of neural activation during a behavioral task. Previous studies have proposed different methods to determine this index, but to our knowledge no studies have compared the performance of these methods. In this study, we compare two established methods with a simpler method proposed here. The aim was to see whether similar results could be achieved with a simpler method and to give an indication of the analysis steps required to determine the LI. A simple unimanual motor task was performed while fTCD was acquired, and the LI determined by each of these methods was compared. In addition, LI determined by each method was related to behavioural output in the form of degree of handedness. The results suggest that although the methods differed in complexity, they yielded similar results when determining the lateralization of motor functions, and its correlation with behavior. Further investigation is needed to expand the conclusions of this preliminary study, however the new method proposed in the paper has great potential as it is much simpler than the more established methods yet yields similar results.

Functional dissociation of ventral frontal and dorsomedial default mode network components during resting state and emotional autobiographical recall.

Humans spend a substantial share of their lives mind-wandering. This spontaneous thinking activity usually comprises autobiographical recall, emotional, and self-referential components. While neuroimaging studies have demonstrated that a specific brain "default mode network" (DMN) is consistently engaged by the "resting state" of the mind, the relative contribution of key cognitive components to DMN activity is still poorly understood. Here we used fMRI to investigate whether activity in neural components of the DMN can be differentially explained by active recall of relevant emotional autobiographical memories as compared with the resting state. Our study design combined emotional autobiographical memory, neutral memory and resting state conditions, separated by a serial subtraction control task. Shared patterns of activation in the DMN were observed in both emotional autobiographical and resting conditions, when compared with serial subtraction. Directly contrasting autobiographical and resting conditions demonstrated a striking dissociation within the DMN in that emotional autobiographical retrieval led to stronger activation of the dorsomedial core regions (medial prefrontal cortex, posterior cingulate cortex), whereas the resting state condition engaged a ventral frontal network (ventral striatum, subgenual and ventral anterior cingulate cortices) in addition to the IPL. Our results reveal an as yet unreported dissociation within the DMN. Whereas the dorsomedial component can be explained by emotional autobiographical memory, the ventral frontal one is predominantly associated with the resting state proper, possibly underlying fundamental motivational mechanisms engaged during spontaneous unconstrained ideation.

Test-retest reliability of fMRI activation generated by different saccade tasks.

PURPOSE: To assess the reproducibility of brain-activation and eye-movement patterns in a saccade paradigm when comparing subjects, tasks, and magnetic resonance (MR) systems. MATERIALS AND METHODS: Forty-five healthy adults at two different sites (n = 45) performed saccade tasks with varying levels of target predictability: predictable (PRED), position predictable (pPRED), time predictable (tPRED), and prosaccade (SAC). Eye-movement pattern was tested with a repeated-measures analysis of variance. Activation maps reproducibility were estimated with the cluster overlap Jaccard index and signal variance coefficient of determination for within-subjects test-retest data, and for between-subjects data from the same and different sites. RESULTS: In all groups latencies increased with decreasing target predictability: PRED < pPRED < tPRED < SAC (P < 0,001). Activation overlap was good to fair (>0.40) in all tasks in the within-subjects test-retest comparisons and poor (<0.40) in the tPRED for different subjects. The overlap of the different tasks for within-groups data was higher (0.40-0.68) than for the between-groups data (0.30-0.50). Activation consistency was 60-85% in the same subjects, 50-79% in different subjects, and 50-80% in different sites. In SAC, the activation found in the same and in different subjects was more consistent than in other tasks (50-80%). CONCLUSION: The predictive saccade tasks produced evidence for brain-activation and eye-movement reproducibility.

A non-parametric statistical test to compare clusters with applications in functional magnetic resonance imaging data.

Statistical inference of functional magnetic resonance imaging (fMRI) data is an important tool in neuroscience investigation. One major hypothesis in neuroscience is that the presence or not of a psychiatric disorder can be explained by the differences in how neurons cluster in the brain. Therefore, it is of interest to verify whether the properties of the clusters change between groups of patients and controls. The usual method to show group differences in brain imaging is to carry out a voxel-wise univariate analysis for a difference between the mean group responses using an appropriate test and to assemble the resulting 'significantly different voxels' into clusters, testing again at cluster level. In this approach, of course, the primary voxel-level test is blind to any cluster structure. Direct assessments of differences between groups at the cluster level seem to be missing in brain imaging. For this reason, we introduce a novel non-parametric statistical test called analysis of cluster structure variability (ANOCVA), which statistically tests whether two or more populations are equally clustered. The proposed method allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering. We illustrate the performance of ANOCVA through simulations and an application to an fMRI dataset composed of children with attention deficit hyperactivity disorder (ADHD) and controls. Results show that there are several differences in the clustering structure of the brain between them. Furthermore, we identify some brain regions previously not described to be involved in the ADHD pathophysiology, generating new hypotheses to be tested. The proposed method is general enough to be applied to other types of datasets, not limited to fMRI, where comparison of clustering structures is of interest.

Causal relationships in longitudinal observational data: An integrative modeling approach.

Much research in psychology relies on data from observational studies that traditionally do not allow for causal interpretation. However, a range of approaches in statistics and computational sciences have been developed to infer causality from correlational data. Based on conceptual and theoretical considerations on the integration of interventional and time-restrainment notions of causality, we set out to design and empirically test a new approach to identify potential causal factors in longitudinal correlational data. A principled and representative set of simulations and an illustrative application to identify early-life determinants of cognitive development in a large cohort study are presented. The simulation results illustrate the potential but also the limitations for discovering causal factors in observational data. In the illustrative application, plausible candidates for early-life determinants of cognitive abilities in 5-year-old children were identified. Based on these results, we discuss the possibilities of using exploratory causal discovery in psychological research but also highlight its limits and potential misuses and misinterpretations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data.

We present a method for real-time recording of human interaction with three-dimensional (3D) virtual objects. The approach consists of associating rotation data of the manipulated object with behavioral measures, such as eye tracking, to make better inferences about the underlying cognitive processes. The task consists of displaying two identical models of the same 3D object (a molecule), presented on a computer screen: a rotating, interactive object (iObj) and a static, target object (tObj). Participants must rotate iObj using the mouse until they consider its orientation to be identical to that of tObj. The computer tracks all interaction data in real time. The participant's gaze data are also recorded using an eye tracker. The measurement frequency is 10 Hz on the computer and 60 Hz on the eye tracker. The orientation data of iObj with respect to tObj are recorded in rotation quaternions. The gaze data are synchronized to the orientation of iObj and referenced using this same system. This method enables us to obtain the following visualizations of the human interaction process with iObj and tObj: (1) angular disparity synchronized with other time-dependent data; (2) 3D rotation trajectory inside what we decided to call a "ball of rotations"; (3) 3D fixation heatmap. All steps of the protocol have used free software, such as GNU Octave and Jmol, and all scripts are available as supplementary material. With this approach, we can conduct detailed quantitative studies of the task-solving process involving mental or physical rotations, rather than only the outcome reached. It is possible to measure precisely how important each part of the 3D models is for the participant in solving tasks, and thus relate the models to relevant variables such as the characteristics of the objects, cognitive abilities of the individuals, and the characteristics of human-machine interface.

A neural signature of affiliative emotion in the human septohypothalamic area.

Comparative studies have established that a number of structures within the rostromedial basal forebrain are critical for affiliative behaviors and social attachment. Lesion and neuroimaging studies concur with the importance of these regions for attachment and the experience of affiliation in humans as well. Yet it remains obscure whether the neural bases of affiliative experiences can be differentiated from the emotional valence with which they are inextricably associated at the experiential level. Here we show, using functional MRI, that kinship-related social scenarios evocative of affiliative emotion induce septal-preoptic-anterior hypothalamic activity that cannot be explained by positive or negative emotional valence alone. Our findings suggest that a phylogenetically conserved ensemble of basal forebrain structures, especially the septohypothalamic area, may play a key role in enabling human affiliative emotion. Our finding of a neural signature of human affiliative experience bears direct implications for the neurobiological mechanisms underpinning impaired affiliative experiences and behaviors in neuropsychiatric conditions.