title: Anatomically-constrained effective connectivity among layers in a cortical column modeled and estimated from local field potentials.
abstract: We propose a neural mass model for anatomically-constrained effective connectivity among neuronal populations residing in four layers (L2/3, L4, L5 and L6) within a cortical column. Eight neuronal populations in a given column--an excitatory population and an inhibitory population per layer--are assumed to be coupled via effective connections of unknown strengths that need to be estimated. The effective connections are constrained to anatomical connections that have been shown to exist in previous anatomical studies. The neural input to a cortical column is directed into the two populations in L4. The anatomically-constrained effective connectivity is captured by a system of 16 stochastic differential equations. Solving these equations yields the average postsynaptic potentials and transmembrane currents generated in each population. The current source density (CSD) responses in each layer, which serve as the model observations, are equated in the model to the sum of all currents generated within that layer. The model is implemented in a continuous-discrete state-space framework, and the innovation method is used for estimating the model parameters from CSD data. To this end, local field potential (LFP) responses to forepaw stimulation were recorded in rat area S1 using multi-channel linear probes. LFPs were converted to CSD signals, which were averaged within each layer, yielding one CSD response per layer. To estimate the effective strengths of connections between all cortical layers, the model was fitted to these CSD signals. The results show that the pattern of effective interactions is strongly influenced by the pattern of strengths of the anatomical connections; however, these two patterns are not identical. The estimated anatomically-constrained effective connectivity matrix and the anatomical connectivity matrix shared five of their six strongest connections, although rankings according to connection strength differed. The strongest effective connections were from excitatory neurons in layer 4 to excitatory neurons in layer 2/3. Our study shows the feasibility of estimating anatomically-constrained effective connectivity within a cortical column, and indicates that there is a strong influence of anatomical connectivity on effective connectivity between cortical layers.
authors.affiliation: Montreal Neurological Institute Brain Imaging Centre, Departments of Neurology and Neurosurgery and Biomedical Engineering, McGill University, Montreal, Quebec, Canada. roberto.soterodiaz@mcgill.ca
authors.forename: Roberto C
authors.initials: RC
authors.lastname: Sotero
authors.forename: Aleksandra
authors.initials: A
authors.lastname: Bortel
authors.forename: Ramón
authors.initials: R
authors.lastname: Martínez-Cancino
authors.forename: Sujaya
authors.initials: S
authors.lastname: Neupane
authors.forename: Peter
authors.initials: P
authors.lastname: O'Connor
authors.forename: Felix
authors.initials: F
authors.lastname: Carbonell
authors.forename: Amir
authors.initials: A
authors.lastname: Shmuel
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D002540:Cerebral Cortex / Q000166:cytology / Q000502:physiology*; D003198:Computer Simulation; D009434:Neural Pathways / Q000166:cytology / Q000502:physiology*; D009474:Neurons / Q000502:physiology; D051381:Rats; D013003:Somatosensory Cortex / Q000166:cytology / Q000502:physiology