Biology subjects

Forlim, C. G.

Publications and source records attributed to Forlim, C. G..

5 recordsLinked to original sources

bioRxiv2020

Locus coeruleus MRI contrast is associated with cortical thickness in older adults

There is growing evidence that neuronal integrity of the noradrenergic locus coeruleus (LC) is important for later-life cognition. Less understood is how LC integrity relates to brain correlates of cognition, such as brain structure. Here, we examined the relationship between cortical thickness and a measure reflecting LC integrity in older (n = 229) and younger adults (n = 67). Using a magnetic resonance imaging sequence which yields high signal intensity in the LC, we assessed the contrast between signal intensity of the LC and that of neighboring pontine reference tissue. The Freesurfer software suite was used to quantify cortical thickness. LC contrast was positively related to cortical thickness in older adults, and this association was prominent in parietal, frontal, and occipital regions. Brain regions where LC contrast was related to cortical thickness include portions of the frontoparietal network which have been implicated in noradrenergically modulated cognitive functions. These findings provide novel evidence for a link between LC structure and cortical brain structure in later adulthood.

neuroscience↗

bioRxiv2019

Efficient small-world and scale-free functional brain networks at rest using k-nearest neighbors thresholding

In recent years, there has been a massive effort to analyze the topological properties of brain networks. Yet, one of the challenging questions in the field is how to construct brain networks based on the connectivity values derived from neuroimaging methods. From a theoretical point of view, it is plausible that the brain would have evolved to minimize energetic costs of information processing, and therefore, maximizes efficiency as well as to redirect its function in an adaptive fashion, that is, resilience. A brain network with such features, when characterized using graph analysis, would present small-world and scale-free properties.\n\nIn this paper, we focused on how the brain network is constructed by introducing and testing an alternative method: k-nearest neighbor (kNN). In addition, we compared the kNN method with one of the most common methods in neuroscience: namely the density threshold. We performed our analyses on functional connectivity matrices derived from resting state fMRI of a big imaging cohort (N=434) of young and older healthy participants. The topology of networks was characterized by the graph measures degree, characteristic path length, clustering coefficient and small world. In addition, we verified whether kNN produces scale-free networks. We showed that networks built by kNN presented advantages over traditional thresholding methods, namely greater values for small-world (linked to efficiency of networks) than those derived by means of density thresholds and moreover, it presented also scale-free properties (linked to the resilience of networks), where density threshold did not. A brain network with such properties would have advantages in terms of efficiency, rapid adaptive reconfiguration and resilience, features of brain networks that are relevant for plasticity and cognition as well as neurological diseases as stroke and dementia.\n\nHighlightsO_LIA novel thresholding method for brain networks based on k-nearest neighbors (kNN)\nC_LIO_LIkNN applied on resting state fMRI from a big cohort of healthy subjects BASE-II\nC_LIO_LIkNN built networks present greater small world properties than density threshold\nC_LIO_LIkNN built networks present scale-free properties whereas density threshold did not\nC_LI

neuroscience↗

bioRxiv2018

Altered resting state functional connectivity in a thalamo-cortico-cerebellar network in patients with schizophrenia

Schizophrenia is described as a disease in which complex psychopathology together with cognitive and behavioral impairments are related to widely disrupted brain circuitry causing a failure in coordinating information across multiple brain sites. This led to the hypothesis of schizophrenia as a network disease e.g. in the cognitive dysmetria model and the dysconnectivity theory. Nevertheless, there is no consensus regarding localized mechanisms, namely dysfunction of certain networks underlying the multifaceted symptomatology.\n\nIn this study, we investigated potential functional disruptions in 35 schizophrenic patients and 41controls using complex cerebral network analysis, namely network-based statistic (NBS) and graph theory in resting state fMRI. NBS can reveal locally impaired subnetworks whereas graph analysis characterizes whole brain network topology.\n\nUsing NBS we observed a local hyperconnected thalamo-cortico-cerebellar subnetwork in the schizophrenia group. Furthermore, nodal graph measures retrieved from the thalamo-cortico-cerebellar subnetwork revealed that the total number of connections from/to (degree) of the thalamus is higher in patients with schizophrenia. Interestingly, graph analysis on the whole brain functional networks did not reveal group differences. Together, our results suggest that disruptions in the brain networks of schizophrenia patients are situated at the local level of the hyperconnected thalamo-cortico-cerebellar rather than globally spread in brain.\n\nOur results provide further evidence for the importance of the thalamus and cerebellum in schizophrenia and to the notion that schizophrenia is a network disease in line with the dysconnectivity theory and cognitive dysmetria model.

neuroscience↗

bioRxiv2018

Reduced Resting-State Connectivity in the Precuneus is correlated with Apathy in Patients with Schizophrenia

A diagnosis of schizophrenia is associated with a heterogeneous psychopathology including positive and negative symptoms. The disconnection hypothesis, an early pathophysiological framework conceptualizes the diversity of symptoms as a result from disconnections in neural networks. In line with this hypothesis, previous neuroimaging studies of patients with schizophrenia reported alterations within the default mode network (DMN), the most prominent network at rest.\n\nAim of the present study was to investigate the functional connectivity during rest in patients with schizophrenia and healthy individuals and explore whether observed functional alterations are related to the psychopathology of patients. Therefore, functional magnetic resonance images at rest were recorded of 35 patients with schizophrenia and 41 healthy individuals. Independent component analysis (ICA) was used to extract resting state networks.\n\nComparing ICA results between groups indicated alterations only within the network of the DMN. More explicitly, reduced connectivity in the precuneus was observed in patients with schizophrenia compared to healthy controls. Connectivity in this area was negatively correlated with the severity of negative symptoms, more specifically with the domain of apathy.\n\nTaken together, the current results provide further evidence for a role DMN alterations might play in schizophrenia and especially in negative symptom such as apathy.

neuroscience↗

bioRxiv2018

New graph-theoretical-multimodal approach using temporal and structural correlations reveal disruption in the thalamo-cortical network in patients with schizophrenia

Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive and behavioral symptoms in this disorder.\n\nWhen building brain networks, functional and structural networks are typically modelled independently: functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks.\n\nIn this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural and topological information of MRI measurements (structural and resting state imaging) to patients with schizophrenia (N=35) and matched healthy individuals (N=41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out.\n\nBy using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole brain connectivity including functional, structural and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting state fMRI, and can be applied to EEG, MEG etc.

neuroscience↗