Spatial representations are processed in the service of several different cognitive functions. The present study capitalizes on the Activation Likelihood Estimation (ALE) method of meta‐analysis to ...identify: (a) the shared neural activations among spatial functions to reveal the “core” network of spatial processing; (b) the specific neural activations associated with each of these functions. Following PRISMA guidelines, a total of 133 fMRI and PET studies were included in the meta‐analysis. The overall analysis showed that the core network of spatial processing comprises regions that are symmetrically distributed on both hemispheres and that include dorsal frontoparietal regions, presupplementary motor area, anterior insula, and frontal operculum. The specific analyses revealed the brain regions that are selectively recruited for each spatial function, such as the right temporoparietal junction for shift of spatial attention, the right parahippocampal gyrus, and the retrosplenial cortex for navigation and spatial long‐term memory. The findings are integrated within a systematic review of the neuroimaging literature and a new neurocognitive model of spatial cognition is proposed.
According to the ATOM (A Theory Of Magnitude), formulated by Walsh more than fifteen years ago, there is a general system of magnitude in the brain that comprises regions, such as the parietal ...cortex, shared by space, time and other magnitudes.
The present meta-analysis of neuroimaging studies used the Activation Likelihood Estimation (ALE) method in order to determine the set of regions commonly activated in space and time processing and to establish the neural activations specific to each magnitude domain. Following PRISMA guidelines, we included in the analysis a total of 112 and 114 experiments, exploring space and time processing, respectively.
We clearly identified the presence of a system of brain regions commonly recruited in both space and time that includes: bilateral insula, the pre-supplementary motor area (pre-SMA), the right frontal operculum and the intraparietal sulci. These regions might be the best candidates to form the core magnitude neural system. Surprisingly, along each of these regions but the insula, ALE values progressed in a cortical gradient from time to space. The SMA exhibited an anterior-posterior gradient, with space activating more-anterior regions (i.e., pre-SMA) and time activating more-posterior regions (i.e., SMA-proper). Frontal and parietal regions showed a dorsal-ventral gradient: space is mediated by dorsal frontal and parietal regions, and time recruits ventral frontal and parietal regions.
Our study supports but also expands the ATOM theory. Therefore, we here re-named it the ‘GradiATOM’ theory (Gradient Theory of Magnitude), proposing that gradient organization can facilitate the transformations and integrations of magnitude representations by allowing space- and time-related neural populations to interact with each other over minimal distances.
Remembering to realize delayed intentions is a multi-phase process, labelled as prospective memory (PM), and involves a plurality of neural networks. The present study utilized the activation ...likelihood estimation method of meta-analysis to provide a complete overview of the brain regions that are consistently activated in each PM phase. We formulated the 'Attention to Delayed Intention' (AtoDI) model to explain the neural dissociation found between intention maintenance and retrieval phases. The dorsal frontoparietal network is involved mainly in the maintenance phase and seems to mediate the strategic monitoring processes, such as the allocation of top-down attention both towards external stimuli, to monitor for the occurrence of the PM cues, and to internal memory contents, to maintain the intention active in memory. The ventral frontoparietal network is recruited in the retrieval phase and might subserve the bottom-up attention captured externally by the PM cues and, internally, by the intention stored in memory. Together with other brain regions (i.e., insula and posterior cingulate cortex), the ventral frontoparietal network would support the spontaneous retrieval processes. The functional contribution of the anterior prefrontal cortex is discussed extensively for each PM phase.
In this study, we propose an approach to detect deception during investigative interviews by integrating response latency and error analysis with the unexpected question technique. Sixty participants ...were assigned to an honest (n = 30) or deceptive group (n = 30). The deceptive group was instructed to memorize the false biographical details of a fictitious identity. Throughout the interviews, participants were presented with a randomized sequence of control, expected, and unexpected open-ended questions about identity. Responses were audio recorded for detailed examination. Our findings indicate that deceptive participants showed markedly longer latencies and higher error rates when answering expected (requiring deception) and unexpected questions (for which premeditated deception was not possible). Longer response latencies were also observed in participants attempting deception when answering control questions (which necessitated truthful answers). Moreover, a within-subject analysis highlighted that responding to unexpected questions significantly impaired individuals' performance compared to answering control and expected questions. Leveraging machine-learning algorithms, our approach attained a classification accuracy of 98% in distinguishing deceptive and honest participants. Additionally, a classification analysis on single response levels was conducted. Our findings underscore the effectiveness of merging response latency metrics and error rates with unexpected questioning as a robust method for identity deception detection in investigative interviews. We also discuss significant implications for enhancing interview strategies.
Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative ...models to investigate brain disorders, including regression, support vector machines and Gaussian process models. With the advance of deep learning technology, the use of deep neural networks has also been proposed. In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimer's disease (n = 206) and mild cognitive impairment (n = 354). We first trained the autoencoder on an independent dataset (UK Biobank dataset) with 11,034 healthy controls. Then, we estimated how each patient deviated from this norm and established which brain regions were associated to this deviation. Finally, we compared the performance of our normative model against traditional classifiers. As expected, we found that patients exhibited deviations according to the severity of their clinical condition. The model identified medial temporal regions, including the hippocampus, and the ventricular system as critical regions for the calculation of the deviation score. Overall, the normative model had comparable cross-cohort generalizability to traditional classifiers. To promote open science, we are making all scripts and the trained models available to the wider research community.
The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the ...methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as ‘brain-age gap’. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders.
Discontinuation of natalizumab in patients with relapsing-remitting multiple sclerosis (RRMS) at risk of progressive multifocal leukoencephalopathy (PML) is associated with disease reactivation. ...Forty-two RRMS patients, who switched from an extended interval dose (EID) of natalizumab to ocrelizumab, underwent magnetic resonance imaging (MRI) and clinical monitoring during washout and after ocrelizumab starting. During the first 3 months, disease reactivation was observed in five (12%) patients; 6 months after ocrelizumab starting, no further relapses were recorded, and Expanded Disability Status Scale (EDSS) remained stable in 38 (90%) patients. In conclusion, ocrelizumab could be considered a choice to mitigate the risk of disease reactivation in patients previously treated with natalizumab-EID.
There is growing evidence that individuals are able to understand others' emotions because they "embody" them, i.e., re-experience them by activating a representation of the observed emotion within ...their own body. One way to study emotion embodiment is provided by a multisensory stimulation paradigm called emotional visual remapping of touch (eVRT), in which the degree of embodiment/remapping of emotions is measured as enhanced detection of near-threshold tactile stimuli on one's own face while viewing different emotional facial expressions. Here, we measured remapping of fear and disgust in participants with low (LA) and high (HA) levels of alexithymia, a personality trait characterized by a difficulty in recognizing emotions. The results showed that fear is remapped in LA but not in HA participants, while disgust is remapped in HA but not in LA participants. To investigate the hypothesis that HA might exhibit increased responses to emotional stimuli producing a heightened physical and visceral sensations, i.e., disgust, in a second experiment we investigated participants' interoceptive abilities and the link between interoception and emotional modulations of VRT. The results showed that participants' disgust modulations of VRT correlated with their ability to perceive bodily signals. We suggest that the emotional profile of HA individuals on the eVRT task could be related to their abnormal tendency to be focalized on their internal bodily signals, and to experience emotions in a "physical" way. Finally, we speculated that these results in HA could be due to a enhancement of insular activity during the perception of disgusted faces.
Remembering to execute pre-defined intentions at the appropriate time in the future is typically referred to as Prospective Memory (PM). Studies of PM showed that distinct cognitive processes ...underlie the execution of delayed intentions depending on whether the cue associated with such intentions is focal to ongoing activity processing or not (i.e., cue focality). The present activation likelihood estimation (ALE) meta-analysis revealed several differences in brain activity as a function of focality of the PM cue. The retrieval of intention is supported mainly by left anterior prefrontal cortex (Brodmann Area, BA 10) in nonfocal tasks, and by cerebellum and ventral parietal regions in focal tasks. Furthermore, the precuneus showed increased activation during the maintenance phase of intentions compared to the retrieval phase in nonfocal tasks, whereas the inferior parietal lobule showed increased activation during the retrieval of intention compared to maintenance phase in the focal tasks. Finally, the retrieval of intention relies more on the activity in anterior cingulate cortex for nonfocal tasks, and on posterior cingulate cortex for focal tasks. Such focality-related pattern of activations suggests that prospective remembering is mediated mainly by top-down and stimulus-independent processes in nonfocal tasks, whereas by more automatic, bottom-up, processes in focal tasks.
Previous studies reported that Multiple Sclerosis (MS) patients treated with natalizumab for one or two years exhibit a significant reduction in relapse rate and in cognitive impairment, but the long ...term effects on cognitive performance are unknown. This study aimed to evaluate the effects of natalizumab on cognitive impairment in a cohort of 24 consecutive patients with relapsing remitting MS treated for 3 years. The neuropsychological tests, as well as relapse number and EDSS, were assessed at baseline and yearly for three years. The impact on cortical atrophy was also considered in a subgroup of them, and are thus to be considered as preliminary. Results showed a significant reduction in the number of impaired neuropsychological tests after three years, a significant decrease in annualized relapse rate at each time points compared to baseline and a stable EDSS. In the neuropsychological assessment, a significant improvement in memory, attention and executive function test scores was detected. Preliminary MRI data show that, while GM volume did not change at 3 years, a significantly greater parahippocampal and prefrontal gray matter density was noticed, the former correlating with neuropsychological improvement in a memory test. This study showed that therapy with Natalizumab is helpful in improving cognitive performance, and is likely to have a protective role on grey matter, over a three years follow-up.
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