Brain network connectivity allowed the unraveling of the functional architecture of the human brain as a complex system composed of several modules interacting with each other. Despite the promising role of connectivity as a surrogate marker of brain damage, the clinical translation and impact have been lower than one would have expected. To date, connectivity measures are not integrated into the clinical assessment of neurological and psychiatric patients. One of the main reasons might depend on time-consuming procedures for data acquisition and analysis, scarcely suitable for the clinical context. Moreover, there is no key-ready package available in the clinical arena for the computation of connectivity measures.
In the last years, different approaches have been developed aimed at assessing brain disconnection following focal lesions by means of a normative publicly available dataset, overcoming the need for diffusion and functional MRI data acquisition from patients. Specifically, these techniques embed the volume of the lesion into a normative functional or structural connectome aimed at estimating indirectly the affected brain pathways. These approaches might have the potential to be widely implemented in the framework of clinical brain lesions. However, these techniques are still in a “germinal” state, and prone to several limitations. Moreover, previous analysis investigated the potentiality of these methods only in stroke patients, leaving open the question of whether several neurological and psychiatric disorders might benefit from these approaches.
In this Research Topic, we wish to collect recent findings with two main aims:
i) extending disconnection approaches to a wide range of neurological and psychiatric disorders
ii) introducing novel approaches to measuring brain disconnections suitable for the clinical setting.
Moreover, this Research Topic will collect studies investigating different techniques aimed at exploring new connectivity measures (e.g., EEG, PET, or structural MRI) and their relevance to the prediction of clinical impairment. Finally, reports comparing the novel and “classical” connectivity techniques are welcome.
Brain network connectivity allowed the unraveling of the functional architecture of the human brain as a complex system composed of several modules interacting with each other. Despite the promising role of connectivity as a surrogate marker of brain damage, the clinical translation and impact have been lower than one would have expected. To date, connectivity measures are not integrated into the clinical assessment of neurological and psychiatric patients. One of the main reasons might depend on time-consuming procedures for data acquisition and analysis, scarcely suitable for the clinical context. Moreover, there is no key-ready package available in the clinical arena for the computation of connectivity measures.
In the last years, different approaches have been developed aimed at assessing brain disconnection following focal lesions by means of a normative publicly available dataset, overcoming the need for diffusion and functional MRI data acquisition from patients. Specifically, these techniques embed the volume of the lesion into a normative functional or structural connectome aimed at estimating indirectly the affected brain pathways. These approaches might have the potential to be widely implemented in the framework of clinical brain lesions. However, these techniques are still in a “germinal” state, and prone to several limitations. Moreover, previous analysis investigated the potentiality of these methods only in stroke patients, leaving open the question of whether several neurological and psychiatric disorders might benefit from these approaches.
In this Research Topic, we wish to collect recent findings with two main aims:
i) extending disconnection approaches to a wide range of neurological and psychiatric disorders
ii) introducing novel approaches to measuring brain disconnections suitable for the clinical setting.
Moreover, this Research Topic will collect studies investigating different techniques aimed at exploring new connectivity measures (e.g., EEG, PET, or structural MRI) and their relevance to the prediction of clinical impairment. Finally, reports comparing the novel and “classical” connectivity techniques are welcome.
