Changes in the superior longitudinal fasciculus and anterior thalamic radiation in the left brain are associated with developmental dyscalculia.

Developmental dyscalculia is a neurodevelopmental disorder specific to arithmetic learning even with normal intelligence and age-appropriate education. Difficulties often persist from childhood through adulthood lowering the individual's quality of life. However, the neural correlates of developmental dyscalculia are poorly understood. This study aimed to identify brain structural connectivity alterations in developmental dyscalculia. All participants were recruited from a large scale, non-referred population sample in a longitudinal design. We studied 10 children with developmental dyscalculia (11.3 ± 0.7 years) and 16 typically developing peers (11.2 ± 0.6 years) using diffusion-weighted magnetic resonance imaging. We assessed white matter microstructure with tract-based spatial statistics in regions-of-interest tracts that had previously been related to math ability in children. Then we used global probabilistic tractography for the first time to measure and compare tract length between developmental dyscalculia and typically developing groups. The high angular resolution diffusion-weighted magnetic resonance imaging and crossing-fiber probabilistic tractography allowed us to evaluate the length of the pathways compared to previous studies. The major findings of our study were reduced white matter coherence and shorter tract length of the left superior longitudinal/arcuate fasciculus and left anterior thalamic radiation in the developmental dyscalculia group. Furthermore, the lower white matter coherence and shorter pathways tended to be associated with the lower math performance. These results from the regional analyses indicate that learning, memory and language-related pathways in the left hemisphere might be related to developmental dyscalculia in children.

Protocol to examine the neural basis of symbolic and non-symbolic quantity processing in human brain with fMRI.

Number perception is among the basic cognitive abilities necessary to understand our environment. Here, we present a protocol to examine the neural underpinnings of numerosity comparison regarding symbolic and non-symbolic stimuli using functional magnetic resonance imaging (fMRI). This protocol gives instructions for screening participants, followed by steps to perform an event-related fMRI experiment and data analysis with SPM12. This protocol will be informative for investigating numerical cognition in various groups including children with dyscalculia or people at different developmental stages. For complete details on the use and execution of this protocol, please refer to Üstün et al. (2021) and Vatansever et al. (2020).

Children With Dyscalculia Show Hippocampal Hyperactivity During Symbolic Number Perception.

Dyscalculia is a learning disability affecting the acquisition of arithmetical skills in children with normal intelligence and age-appropriate education. Two hypotheses attempt to explain the main cause of dyscalculia. The first hypothesis suggests that a problem with the core mechanisms of perceiving (non-symbolic) quantities is the cause of dyscalculia (core deficit hypothesis), while the alternative hypothesis suggests that dyscalculics have problems only with the processing of numerical symbols (access deficit hypothesis). In the present study, the symbolic and non-symbolic numerosity processing of typically developing children and children with dyscalculia were examined with functional magnetic resonance imaging (fMRI). Control (n = 15, mean age: 11.26) and dyscalculia (n = 12, mean age: 11.25) groups were determined using a wide-scale screening process. Participants performed a quantity comparison paradigm in the fMRI with two number conditions (dot and symbol comparison) and two difficulty levels (0.5 and 0.7 ratio). The results showed that the bilateral intraparietal sulcus (IPS), left dorsolateral prefrontal cortex (DLPFC) and left fusiform gyrus (so-called "number form area") were activated for number perception as well as bilateral occipital and supplementary motor areas. The task difficulty engaged bilateral insular cortex, anterior cingulate cortex, IPS, and DLPFC activation. The dyscalculia group showed more activation in the left orbitofrontal cortex, left medial prefrontal cortex, and right anterior cingulate cortex than the control group. The dyscalculia group showed left hippocampus activation specifically for the symbolic condition. Increased left hippocampal and left-lateralized frontal network activation suggest increased executive and memory-based compensation mechanisms during symbolic processing for dyscalculics. Overall, our findings support the access deficit hypothesis as a neural basis for dyscalculia.

Amygdala-prefrontal cortex connectivity increased during face discrimination but not time perception.

Time sensitivity is affected by emotional stimuli such as fearful faces. The effect of threatening stimuli on time perception depends on numerous factors, including task type and duration range. We applied a two-interval forced-choice task using face stimuli to healthy volunteers to evaluate time perception and emotion interaction using functional magnetic resonance imaging. We conducted finite impulse response analysis to examine time series for the significantly activated brain areas and psycho-physical interaction to investigate the connectivity between selected regions. Time perception engaged a right-lateralised frontoparietal network, while a face discrimination task activated the amygdala and fusiform face area (FFA). No voxels were active with regard to the effect of expression (fearful versus neutral). In parallel with this, our behavioural results showed that attending to the fearful faces did not cause duration overestimation. Finally, connectivity of the amygdala and FFA to the middle frontal gyrus increased during the face processing condition compared to the timing task. Overall, our results suggest that the prefrontal-amygdala connectivity might be required for the emotional processing of facial stimuli. On the other hand, attentional load, task type and task difficulty are discussed as possible factors that influence the effects of emotion on time perception.

Integrated assessment of visual perception abnormalities in psychotic disorders and relationship with clinical characteristics.

BACKGROUND: The visual system is recognized as an important site of pathology and dysfunction in schizophrenia. In this study, we evaluated different visual perceptual functions in patients with psychotic disorders using a potentially clinically applicable task battery and assessed their relationship with symptom severity in patients, and with schizotypal features in healthy participants. METHODS: Five different areas of visual functioning were evaluated in patients with schizophrenia and schizoaffective disorder (n = 28) and healthy control subjects (n = 31) using a battery that included visuospatial working memory (VSWM), velocity discrimination (VD), contour integration, visual context processing, and backward masking tasks. RESULTS: The patient group demonstrated significantly lower performance in VD, contour integration, and VSWM tasks. Performance did not differ between the two groups on the visual context processing task and did not differ across levels of interstimulus intervals in the backward masking task. Performances on VSWM, VD, and contour integration tasks were correlated with negative symptom severity but not with other symptom dimensions in the patient group. VSWM and VD performances were also correlated with negative sychizotypal features in healthy controls. CONCLUSION: Taken together, these results demonstrate significant abnormalities in multiple visual processing tasks in patients with psychotic disorders, adding to the literature implicating visual abnormalities in these conditions. Furthermore, our results show that visual processing impairments are associated with the negative symptom dimension in patients as well as healthy individuals.

Neural Mechanisms Underlying Time Perception and Reward Anticipation.

Findings suggest that the physiological mechanisms involved in the reward anticipation and time perception partially overlap. But the systematic investigation of a potential interaction between time and reward systems using neuroimaging is lacking. Eighteen healthy volunteers (all right-handed) participated in an event-related functional magnetic resonance imaging (fMRI) experiment that employs a visual paradigm that consists monetary reward to assess whether the functional neural representations of time perception and reward prospection are shared or distinct. Subjects performed a time perception task in which observers had to extrapolate the velocity of an occluded moving object in "reward" vs. "no-reward" sessions during fMRI scanning. There were also "control condition" trials in which participants judged about the color tone change of the stimuli. Time perception showed a fronto-parietal (more extensive in the right) cingulate and peristriate cortical as well as cerebellar activity. On the other hand, reward anticipation activated anterior insular cortex, nucleus accumbens, caudate nucleus, thalamus, cerebellum, postcentral gyrus, and peristriate cortex. Interaction between the time perception and the reward prospect showed dorsolateral, orbitofrontal, medial prefrontal and caudate nucleus activity. Our findings suggest that a prefrontal-striatal circuit might integrate reward and timing systems of the brain.

Neural Networks for Time Perception and Working Memory.

Time is an important concept which determines most human behaviors, however questions remain about how time is perceived and which areas of the brain are responsible for time perception. The aim of this study was to evaluate the relationship between time perception and working memory in healthy adults. Functional magnetic resonance imaging (fMRI) was used during the application of a visual paradigm. In all of the conditions, the participants were presented with a moving black rectangle on a gray screen. The rectangle was obstructed by a black bar for a time period and then reappeared again. During different conditions, participants (n = 15, eight male) responded according to the instructions they were given, including details about time and the working memory or dual task requirements. The results showed activations in right dorsolateral prefrontal and right intraparietal cortical networks, together with the anterior cingulate cortex (ACC), anterior insula and basal ganglia (BG) during time perception. On the other hand, working memory engaged the left prefrontal cortex, ACC, left superior parietal cortex, BG and cerebellum activity. Both time perception and working memory were related to a strong peristriate cortical activity. On the other hand, the interaction of time and memory showed activity in the intraparietal sulcus (IPS) and posterior cingulate cortex (PCC). These results support a distributed neural network based model for time perception and that the intraparietal and posterior cingulate areas might play a role in the interface of memory and timing.

Automatic segmentation of human facial tissue by MRI-CT fusion: a feasibility study.

The aim of this study was to develop automatic image segmentation methods to segment human facial tissue which contains very thin anatomic structures. The segmentation output can be used to construct a more realistic human face model for a variety of purposes like surgery planning, patient specific prosthesis design and facial expression simulation. Segmentation methods developed were based on Bayesian and Level Set frameworks, which were applied on three image types: magnetic resonance imaging (MRI), computerized tomography (CT) and fusion, in which case information from both modalities were utilized maximally for every tissue type. The results on human data indicated that fusion, thickness adaptive and postprocessing options provided the best muscle/fat segmentation scores in both Level Set and Bayesian methods. When the best Level Set and Bayesian methods were compared, scores of the latter were better. Number of algorithm parameters (to be trained) and computer run time measured were also in favour of the Bayesian method.