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Interpreting the effects of altered brain anatomical connectivity on fMRI functional connectivity: a role for computational neural modeling.

Horwitz B, Hwang C, Alstott J - Front Hum Neurosci (2013)

Bottom Line: Many of these simulation studies found that structural connectivity changes do not necessarily result in matching changes in functional/effective connectivity in the areas of structural modification.Often, it was observed that increases in functional/effective connectivity in the altered brain did not necessarily correspond to increases in the strength of the anatomical connection weights.These results suggest that this interpretation can be mistaken.

View Article: PubMed Central - PubMed

Affiliation: Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health Bethesda, MD, USA.

ABSTRACT
Recently, there have been a large number of studies using resting state fMRI to characterize abnormal brain connectivity in patients with a variety of neurological, psychiatric, and developmental disorders. However, interpreting what the differences in resting state fMRI functional connectivity (rsfMRI-FC) actually reflect in terms of the underlying neural pathology has proved to be elusive because of the complexity of brain anatomical connectivity. The same is the case for task-based fMRI studies. In the last few years, several groups have used large-scale neural modeling to help provide some insight into the relationship between brain anatomical connectivity and the corresponding patterns of fMRI-FC. In this paper we review several efforts at using large-scale neural modeling to investigate the relationship between structural connectivity and functional/effective connectivity to determine how alterations in structural connectivity are manifested in altered patterns of functional/effective connectivity. Because the alterations made in the anatomical connectivity between specific brain regions in the model are known in detail, one can use the results of these simulations to determine the corresponding alterations in rsfMRI-FC. Many of these simulation studies found that structural connectivity changes do not necessarily result in matching changes in functional/effective connectivity in the areas of structural modification. Often, it was observed that increases in functional/effective connectivity in the altered brain did not necessarily correspond to increases in the strength of the anatomical connection weights. Note that increases in rsfMRI-FC in patients have been interpreted in some cases as resulting from neural plasticity. These results suggest that this interpretation can be mistaken. The relevance of these simulation findings to the use of functional/effective fMRI connectivity as biomarkers for brain disorders is also discussed.

No MeSH data available.


Related in: MedlinePlus

Brain connectivity matrices. (A) Structural connectivity matrix among the set of 998 ROIs of the average of the DSI data of five normal subjects of Hagmann et al. (2008). (B) Functional connectivity matrix of Pearson correlations from the computational model used by Honey et al. (2009) and Alstott et al. (2009) for the averaged structural matrix of (A), showing relatively high simulated rsfMRI-FC within lobes, and lower rsfMRI-FC between hemispheres.
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Figure 2: Brain connectivity matrices. (A) Structural connectivity matrix among the set of 998 ROIs of the average of the DSI data of five normal subjects of Hagmann et al. (2008). (B) Functional connectivity matrix of Pearson correlations from the computational model used by Honey et al. (2009) and Alstott et al. (2009) for the averaged structural matrix of (A), showing relatively high simulated rsfMRI-FC within lobes, and lower rsfMRI-FC between hemispheres.

Mentions: An example of simulating human rsfMRI data was provided by Honey et al. (2009). They used a structural model based on diffusion spectrum imaging (DSI) data obtained from five normal human participants originally described by Hagmann et al. (2008) (see Figure 2A)3. The structural connections were evaluated from streamline tractography values between each pair of 998 cortical regions. The neural model assigned to each of these regions employed the neural mass model of Breakspear et al. (2003), which represents an ensemble of excitatory and inhibitory neurons possessing both ligand-gated and voltage-gated membrane channels. A non-linear hemodynamic model was used to convert simulated neural activity into simulated BOLD fMRI data (Friston et al., 2000) (see Figure 2B). Honey et al. (2009) used this formulation to compare simulated rsfMRI data against actual fMRI data obtained in the same subjects from whom the DSI data were acquired. Their main conclusion was that in both the simulated and experimental data, the underlying structural connectivity constrained the pattern of resting state functional connectivity, although some functional connectivity between non-anatomically connected regions was also present. These findings were supported by a resting state fMRI functional connectivity (rsfMRI-FC) study in monkey by Adachi et al. (2012), who also performed a simulation study employing the modeling framework of an earlier Honey et al. paper (2007).


Interpreting the effects of altered brain anatomical connectivity on fMRI functional connectivity: a role for computational neural modeling.

Horwitz B, Hwang C, Alstott J - Front Hum Neurosci (2013)

Brain connectivity matrices. (A) Structural connectivity matrix among the set of 998 ROIs of the average of the DSI data of five normal subjects of Hagmann et al. (2008). (B) Functional connectivity matrix of Pearson correlations from the computational model used by Honey et al. (2009) and Alstott et al. (2009) for the averaged structural matrix of (A), showing relatively high simulated rsfMRI-FC within lobes, and lower rsfMRI-FC between hemispheres.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3822330&req=5

Figure 2: Brain connectivity matrices. (A) Structural connectivity matrix among the set of 998 ROIs of the average of the DSI data of five normal subjects of Hagmann et al. (2008). (B) Functional connectivity matrix of Pearson correlations from the computational model used by Honey et al. (2009) and Alstott et al. (2009) for the averaged structural matrix of (A), showing relatively high simulated rsfMRI-FC within lobes, and lower rsfMRI-FC between hemispheres.
Mentions: An example of simulating human rsfMRI data was provided by Honey et al. (2009). They used a structural model based on diffusion spectrum imaging (DSI) data obtained from five normal human participants originally described by Hagmann et al. (2008) (see Figure 2A)3. The structural connections were evaluated from streamline tractography values between each pair of 998 cortical regions. The neural model assigned to each of these regions employed the neural mass model of Breakspear et al. (2003), which represents an ensemble of excitatory and inhibitory neurons possessing both ligand-gated and voltage-gated membrane channels. A non-linear hemodynamic model was used to convert simulated neural activity into simulated BOLD fMRI data (Friston et al., 2000) (see Figure 2B). Honey et al. (2009) used this formulation to compare simulated rsfMRI data against actual fMRI data obtained in the same subjects from whom the DSI data were acquired. Their main conclusion was that in both the simulated and experimental data, the underlying structural connectivity constrained the pattern of resting state functional connectivity, although some functional connectivity between non-anatomically connected regions was also present. These findings were supported by a resting state fMRI functional connectivity (rsfMRI-FC) study in monkey by Adachi et al. (2012), who also performed a simulation study employing the modeling framework of an earlier Honey et al. paper (2007).

Bottom Line: Many of these simulation studies found that structural connectivity changes do not necessarily result in matching changes in functional/effective connectivity in the areas of structural modification.Often, it was observed that increases in functional/effective connectivity in the altered brain did not necessarily correspond to increases in the strength of the anatomical connection weights.These results suggest that this interpretation can be mistaken.

View Article: PubMed Central - PubMed

Affiliation: Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health Bethesda, MD, USA.

ABSTRACT
Recently, there have been a large number of studies using resting state fMRI to characterize abnormal brain connectivity in patients with a variety of neurological, psychiatric, and developmental disorders. However, interpreting what the differences in resting state fMRI functional connectivity (rsfMRI-FC) actually reflect in terms of the underlying neural pathology has proved to be elusive because of the complexity of brain anatomical connectivity. The same is the case for task-based fMRI studies. In the last few years, several groups have used large-scale neural modeling to help provide some insight into the relationship between brain anatomical connectivity and the corresponding patterns of fMRI-FC. In this paper we review several efforts at using large-scale neural modeling to investigate the relationship between structural connectivity and functional/effective connectivity to determine how alterations in structural connectivity are manifested in altered patterns of functional/effective connectivity. Because the alterations made in the anatomical connectivity between specific brain regions in the model are known in detail, one can use the results of these simulations to determine the corresponding alterations in rsfMRI-FC. Many of these simulation studies found that structural connectivity changes do not necessarily result in matching changes in functional/effective connectivity in the areas of structural modification. Often, it was observed that increases in functional/effective connectivity in the altered brain did not necessarily correspond to increases in the strength of the anatomical connection weights. Note that increases in rsfMRI-FC in patients have been interpreted in some cases as resulting from neural plasticity. These results suggest that this interpretation can be mistaken. The relevance of these simulation findings to the use of functional/effective fMRI connectivity as biomarkers for brain disorders is also discussed.

No MeSH data available.


Related in: MedlinePlus