RESUMEN
In patients with relapsing-remitting multiple sclerosis (RRMS), interferon beta-1b (IFNß-1b) reduces the occurrence of contrast enhancing lesions (CELs) on magnetic resonance imaging (MRI). Questions remain on the stability of IFNß-1b effect over time and its action beyond the reduction of CELs. In this study, we described the IFNß-1b effect by a mixed effects model, quantifying the interpatient variability associated with its parameters. Using a negative binomial distribution model as a natural history model, the effect of IFNß-1b was evaluated using different mathematical functions of time. IFNß-1b produced a decrease in the expected CEL numbers, inhibiting the formation of new CELs but did not promote the resolution of the already-formed ones. Based on the final selected model, simulations were carried out to optimize the combined IFNß-1b-corticosteroid therapy as a proof-of-concept. In summary, we provide evidence on the dynamics of CELs under IFNß-1b treatment that can be used to monitor the effects of therapies in MS.
RESUMEN
BACKGROUND: Fatigue is one of the most frequent and disturbing symptoms in multiple sclerosis (MS), directly affecting the patient's quality of life. However, many questions remain unclear regarding the anatomic brain correlate of MS-related fatigue. OBJECTIVE: To assess the relationship between fatigue and white matter lesion location and gray matter atrophy. METHODS: In this study, 60 patients with MS were evaluated with the Modified Fatigue Impact Scale and magnetic resonance imaging. Location of white matter lesion was analyzed using a voxel-by-voxel lesion probability mapping approach and gray matter atrophy degree and location using an optimized voxel-based morphometry method. RESULTS: We found a correlation between lesion load and fatigue score (T2 lesion load: r=0.415, P=0.001; T1 lesion load r=0.328, P=0.011). Moreover, fatigue correlated with lesions in the right parietotemporal (periatrial area, juxtaventricular white matter deep in the parietal lobe and callosal forceps) and left frontal (middle-anterior corpus callosum, anterior cingulum and centrum semiovale of the superior and middle frontal gyri) white matter regions (P<0.001 in all cases). Finally, fatigue score significantly correlated with gray matter atrophy in frontal regions, specifically, the left superior frontal gyrus and bilateral middle frontal gyri (P<0.001 in all cases). CONCLUSION: Our results suggest that the symptom of fatigue is associated with a disruption of brain networks involved in cognitive/attentional processes.
Asunto(s)
Fatiga/etiología , Fatiga/patología , Lóbulo Frontal/patología , Esclerosis Múltiple Recurrente-Remitente/complicaciones , Esclerosis Múltiple Recurrente-Remitente/patología , Lóbulo Parietal/patología , Adulto , Atrofia , Femenino , Humanos , Imagen por Resonancia Magnética , Masculino , Persona de Mediana Edad , Esclerosis Múltiple Crónica Progresiva/complicaciones , Esclerosis Múltiple Crónica Progresiva/patología , Vías Nerviosas/patología , Pruebas NeuropsicológicasRESUMEN
The behaviors of autism overlap with a diverse array of other neurological disorders, suggesting common molecular mechanisms. We conducted a large comparative analysis of the network of genes linked to autism with those of 432 other neurological diseases to circumscribe a multi-disorder subcomponent of autism. We leveraged the biological process and interaction properties of these multi-disorder autism genes to overcome the across-the-board multiple hypothesis corrections that a purely data-driven approach requires. Using prior knowledge of biological process, we identified 154 genes not previously linked to autism of which 42% were significantly differentially expressed in autistic individuals. Then, using prior knowledge from interaction networks of disorders related to autism, we uncovered 334 new genes that interact with published autism genes, of which 87% were significantly differentially regulated in autistic individuals. Our analysis provided a novel picture of autism from the perspective of related neurological disorders and suggested a model by which prior knowledge of interaction networks can inform and focus genome-scale studies of complex neurological disorders.