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The Deep Brain Connectome
Published in Yu Chen, Babak Kateb, Neurophotonics and Brain Mapping, 2017
Ifije E. Ohiorhenuan, Vance L. Fredrickson, Mark A. Liker
In addition to acting as a biomarker of disease severity and identifying new treatment targets, connectome identified in post-DBS patients may be helpful for improving targeting as well as tracking responsiveness to treatment. In one illustrative case, the subthalamopontocerebellar and dentatothalamic tracts were mapped out in patients with Parkinson’s disease undergoing implantation of subthalamic electrodes (Sweet et al. 2014). The authors found a trend toward better postoperative tremor control in patients where the active contact was closer to the dentatothalamic tract. Although, due to the small sample size, this trend did not reach significance, this study offers an example of how fiber tractography may one day be used to help refine current surgical practices. Similarly, pre- and post-surgical connectomes are starting to shed light on the underpinnings of patient’s responsiveness to DBS and suggesting imaging markers for tracking treatment effectiveness. In one case series, DBS-mediated improvement of certain Parkinsonian symptoms was associated with structural and functional changes in connectivity (van Hartevelt et al. 2014). In another, patients with mesial temporal lobe epilepsy who were seizure-free following surgery were found to have a different pre-surgical pattern of connectivity within the thalamocortical network from patients with persistent seizures after surgery (Ji et al. 2015). Moreover, the surgical control of seizures was associated with widespread reorganization of these patients’ connectomes. These studies relied on brain connectivity measures from graph theory as well as computational modeling, underscoring the fact that the true power of connectomics can only be realized by combining structural connectivity maps with theoretical models of brain activity.
Biomarkers for Organophosphate Poisoning: Physiological and Pathological Responses
Published in Brian J. Lukey, James A. Romano, Salem Harry, Chemical Warfare Agents, 2019
Arik Eisenkraft, Avshalom Falk, Kevin G. McGarry Jr.
Diffusion-weighted imaging detects cytotoxic edema in the early acute phase post soman- or pilocarpine-induced SE and periictal imaging of humans after SE. This pathological finding has been proposed as a predictor of brain damage in animals and worse prognosis in human SE. Two recent longitudinal studies, one in KA-induced epileptogenesis in mice (Janz et al., 2017) and the other in DFP-induced epileptogenesis in rats (Hobson et al., 2017), have shown that diffusion imaging is a useful biomarker during all phases of disease processes, using DTI for imaging and novel statistical analysis tools. In other imaging studies, diffusion imaging was employed to characterize post-SE brain plasticity changes (Gröhn et al., 2011). DTI can characterize microstructural changes with better resolution and precision than standard DW-MRI and has not been discussed in depth in this chapter. Retrospective DTI analyses in victims of the 1995 Tokyo terror attack and controls revealed significantly decreased fractional anisotropy in widespread brain regions, including some that correlated with the severity of somatic complaints (Yamasue et al., 2007). Similarly, different associations between DTI parameters and performance in neuropsychological tests were observed in GB/GF-exposed and non-exposed Gulf War veterans (Chao et al., 2015). Both studies show the power of DTI in detecting possible structural correlates of long-lasting physical and cognitive adverse consequences of CWNA exposure in humans. DTI and the related technique of fiber tractography, in conjunction with other functional modalities, mainly functional magnetic resonance imaging (fMRI), formed the research field of brain connectomics, which has a growing role in the study of cognitive co-morbidities and the development of clinical applications, including biomarker discovery (Engel et al., 2013). DTI studies in human TLE revealed abnormalities in extratemporal white matter regions, which were not seen with conventional MRI (Gross, 2011; Yogarajah and Duncan, 2008; Yogarajah et al., 2008). Quantitative DTI studies in TLE patients showed correlations between diffusion parameters (fractional anisotropy and mean diffusivity) in specific white matter tracts and language or memory performance test results (McDonald et al., 2014; Leyden et al., 2015). In addition to patient studies, functional connectivity studies have been extended to animal epilepsy models (Otte et al., 2012) that afford longitudinal studies, which are essential for biomarker discovery.
Diffusion Magnetic Resonance Imaging in the Central Nervous System
Published in Shoogo Ueno, Bioimaging, 2020
Kouhei Kamiya, Yuichi Suzuki, Osamu Abe
Another modern application of tractography is the field of connectomics, i.e., the study of the brain as a network (Hagmann et al., 2008; Sporns, Tononi, & Kötter, 2005). Terms used for brain network analysis are derived from graph theory, a field of mathematics with a long history that deals with complex networks. Graph theory describes a network by its nodes and edges. In the brain network, nodes represent cortical and subcortical gray matter regions, whereas edges typically represent the white matter fiber bundles that connect pairs of regions. Thus, building a connectome with dMRI basically consists of three steps: 1) defining nodes; 2) mapping edges; and 3) quantifying edges (Figure 6.13). First, nodes are defined by either registering pre-defined atlases to individual brains or by data-driven approaches (Glasser et al., 2016). Then, dMRI tractography is used to estimate edges (the connections between nodes). Finally, an N-by-N connectivity matrix, where N is the number of nodes, is reconstructed. If we are after a binary matrix, a certain threshold is applied so that each cell of the matrix contains either 0 or 1 (absence/presence of an edge). Alternatively, one may reconstruct a weighted matrix, where each cell contains a measure of the relative weight of that edge (e.g., streamline count). The connectivity matrix can be either analyzed directly (e.g., comparing the weight of each edge between patients and controls) or further fed into a graph-theory framework to derive indices of network characteristics (Rubinov & Sporns, 2010). Moreover, connectivity matrices can be reconstructed with modalities other than dMRI, such as neuroanatomy, electroencephalography, magnetoencephalography, and functional MRI, and compared among different modalities. During the last decade, interesting features of the brain network have been discovered. For example, the brain is a small‐world network (Bassett & Bullmore, 2006), characterized by communities of densely interconnected nodes (segregation) and sparse connections among different communities via a small number of long‐distance connections (integration). Also, the presence of densely interconnected hub nodes (a rich club) has been reported (van den Heuvel & Sporns, 2011). Such properties of the brain network are understood from the viewpoint of a trade-off between network efficiency and network cost (Bullmore & Sporns, 2012). Studies comparing patients and controls have provided intriguing results demonstrating alterations of these network properties in diseases [for reviews, see Fornito et al. (2015)].
The human functional connectome in neurodegenerative diseases: relationship to pathology and clinical progression
Published in Expert Review of Neurotherapeutics, 2023
Massimo Filippi, Edoardo Gioele Spinelli, Camilla Cividini, Alma Ghirelli, Silvia Basaia, Federica Agosta
Through this review, we are aiming to show that the evaluation of the functional connectome allows not only to explore and validate models of pathological propagation, but also to answer fundamental clinical questions and needs in the context of the main neurodegenerative diseases. In contrast with the established use of brain volumetric or diffusivity measures to assess brain structural alterations in neurodegenerative disease [22], in this review we will demonstrate the great, yet mostly unexpressed potential of functional connectomic techniques in this field. First, we will highlight that studying the brain functional connectomic architecture has proven its utility to describe characteristic alterations that may support the clinical diagnosis of these conditions. Secondly, evidence suggesting the use of such techniques for monitoring and tracking in vivo changes along the disease course will be reviewed, as well as studies assessing potential use for prognostic stratification and prediction of treatment response. Finally, we will give space to the most recent and innovative connectomic techniques to investigate more in depth the functioning and the organization of the functional connectome, in order to build more accurate disease spreading models. Reaching these goals will help in developing tailored and targeted therapies for highly prevalent neurodegenerative diseases that, to date, are still unrelenting.
Postoperative Focal Lower Extremity Supplementary Motor Area Syndrome: Case Report and Review of the Literature
Published in The Neurodiagnostic Journal, 2021
Nicholas B. Dadario, Joanna K. Tabor, Justin Silverstein, Xiaonan R. Sun, Randy S. DAmico
Recently updated surface-based, multi-modal parcellation schemes have expanded our understanding of the organization of human cortical anatomy as well as the cortico-cortical connectivity fibers involved in the processing and integration of complex information (Glasser et al. 2016). Cortical models suggest the SMA comprises four cortical regions: the superior frontal language area (SFL), the supplementary and cingulate eye field (SCEF), Brodmann Area 6 medial anterior (6 ma) and Brodmann Area 6 medial posterior (6mp) (Glasser et al. 2016). These regions demonstrate extensive connections within and outside of the motor network, together supporting a known role of the SMA as a relay center for the initiation and production of ordered movement and language (Sheets et al. 2021). Strong connectivity can be seen with the ipsilateral primary motor cortex, inferior and middle frontal gyri, the anterior cingulate cortex and the insula, as well as many connections to the contralateral SMA, the anterior cingulate cortex, the lateral premotor cortex, and the inferior frontal gyrus. In particular, the white matter connections from this region can be seen mainly with (1) the frontal aslant tract (FAT), (2) the pyramidal tracts, and (3) transcallosal fibers extending from the contralateral hemisphere (Briggs et al. 2018; La Corte et al. 2021). While intraoperative tractography was not performed in the current case, published connectomic data provide an important framework that allows us to better understand and hypothesize about our patient’s neurologic deficit for future study (Glasser et al. 2016).
A Cortical Parcellation Based Analysis of Ventral Premotor Area Connectivity
Published in Neurological Research, 2021
John R. Sheets, Robert G. Briggs, Nicholas B. Dadario, Isabella M. Young, Michael Y. Bai, Anujan Poologaindran, Cordell M. Baker, Andrew K. Conner, Michael E. Sughrue
Furthermore, it is important to note that this manuscript was designed to be predominantly a qualitative, descriptive paper to provide anatomic information that encourages further study on functional relevance. Therefore, while connectomics allows the modeling of the common connections in an area of interest with strong prediction values [83], there are individual variations that may offer additional connections not readily apparent in connectomic analyses [84]. Still, connectomic analyses of large data sets allowed us the ability to observe possible relationships between cortical structures otherwise invisible to the human eye, such as the interhemispheric asymmetry identified between area 6v and 55b, which could provide insight into further study for future clinical applications. However, quantitative analyses from diffusion tractography should be viewed cautiously despite its ability to model qualitative analyses well due to limitations with individual uniqueness and differences.