I’d also be interested in your take on “neuronavigation” of the sort utilized in the SAINT trial of accelerated TMS and now being commercialized by Magnus Medical. Does your critique apply? Or is the SAINT approach individualized in a way that avoids the pitfalls of typical neuroimaging?
I have to confess that this isn’t my area of expertise but it seems like neuro-stimulation is a whole different kettle of fish and does not have the same issues of ‘brain wide association studies’. By targeting specific brain circuits, this approach seems much more individualised.
I’m less convinced we will be able to identify relevant circuits that can be targeted in disorders like schizophrenia - maybe for negative symptoms, who knows!
Any thoughts on how this might be relevant for TMS as a treatment for depression and OCD (and other conditions). As I understand it, the treatment targets (eg. DLPFC) have been partly chosen because of neuroimaging findings on apparently relevant circuits. How might these critiques of typical neuroimaging approaches impact the choice of treatment targets in TMS? Thanks!
Nicely presented. the critique of localisation and averaging is not really new
i) "they represent connectivity hubs" rather than functionally specialised systems. this is what we would expect in network architectures. see e.g Pessoa. and every analysis of artificial neural networks.
ii) re averaging: this methodological point about voxel averaging has been made from the outset.
imaging can only be part of a battery of techniques. no imaging study on its own will suffice.
re idiosyncratic brain organization. this is why the classic neuropsychological studies are "single case"
I can believe that none of these critiques are new, but it seems that the field of neuroimaging (or some subfields) have taken wrong turns and are now being redirected.
I suppose the question though is even if we can map specific symptoms to anatomical regions (i.e., "localizing the lesion"), does this (1) make a difference in treatment outcomes? and (2) is this kind of imaging feasible to do in a routine real-world clinical setting? My suspicion is the answer is no.
Another thought: the schizophrenia studies you cited have tiny sample sizes or are case reports. Is this convincing enough to say that we have "mapped the symptoms of schizophrenia?" I suspect most researchers and clinicians will say "we need a larger sample size!" Therein lies the conundrum: as the sample size goes up, the more of an averaging effect will occur. Additionally, how much of the fMRI findings are reflecting the individual's own cerebral blood flow changes independent of pathology, or actually a sign of the pathology we are trying to understand? As we all know, fMRI as an analysis method has its own set of problems with reproducibility of findings. If you torture the data long enough, it will confess to anything.
Whether we go big or go small with our data, I suspect we will run into problems either way trying to decipher neuroimaging data for most psychiatric disorders. This is where I hope autoimmune biomarkers will provide some progress (e.g., anti-NMDAR). This has already helped advanced care for individuals with atypical presentations of psychosis, who would probably have instead been diagnosed with "schizophrenia" 30 years ago. Hopefully through more understanding in this lens, the false construct of schizophrenia can slowly be chipped away at.
I pretty much agree with everything you've said here. Though worth noting that Dosenbach's homunculus study also had a tiny sample size. Maybe if the questions/methods are super-precise, we can sacrifice the generalisability of larger, representative samples. I think you're right that there isn't an obvious immediate clinical application.
In the 60s and 70s there were a large number of findings on atypical phenylketonuria (42 forms). In the 80s we found that these explain ¾ of all chronic psychoses - until the pharmaceutical companies stifled all research with serotonin
I’d also be interested in your take on “neuronavigation” of the sort utilized in the SAINT trial of accelerated TMS and now being commercialized by Magnus Medical. Does your critique apply? Or is the SAINT approach individualized in a way that avoids the pitfalls of typical neuroimaging?
Great point.
I have to confess that this isn’t my area of expertise but it seems like neuro-stimulation is a whole different kettle of fish and does not have the same issues of ‘brain wide association studies’. By targeting specific brain circuits, this approach seems much more individualised.
I’m less convinced we will be able to identify relevant circuits that can be targeted in disorders like schizophrenia - maybe for negative symptoms, who knows!
Any thoughts on how this might be relevant for TMS as a treatment for depression and OCD (and other conditions). As I understand it, the treatment targets (eg. DLPFC) have been partly chosen because of neuroimaging findings on apparently relevant circuits. How might these critiques of typical neuroimaging approaches impact the choice of treatment targets in TMS? Thanks!
Nicely presented. the critique of localisation and averaging is not really new
i) "they represent connectivity hubs" rather than functionally specialised systems. this is what we would expect in network architectures. see e.g Pessoa. and every analysis of artificial neural networks.
ii) re averaging: this methodological point about voxel averaging has been made from the outset.
imaging can only be part of a battery of techniques. no imaging study on its own will suffice.
re idiosyncratic brain organization. this is why the classic neuropsychological studies are "single case"
Thanks for this perspective.
I can believe that none of these critiques are new, but it seems that the field of neuroimaging (or some subfields) have taken wrong turns and are now being redirected.
Great analysis.
I suppose the question though is even if we can map specific symptoms to anatomical regions (i.e., "localizing the lesion"), does this (1) make a difference in treatment outcomes? and (2) is this kind of imaging feasible to do in a routine real-world clinical setting? My suspicion is the answer is no.
Another thought: the schizophrenia studies you cited have tiny sample sizes or are case reports. Is this convincing enough to say that we have "mapped the symptoms of schizophrenia?" I suspect most researchers and clinicians will say "we need a larger sample size!" Therein lies the conundrum: as the sample size goes up, the more of an averaging effect will occur. Additionally, how much of the fMRI findings are reflecting the individual's own cerebral blood flow changes independent of pathology, or actually a sign of the pathology we are trying to understand? As we all know, fMRI as an analysis method has its own set of problems with reproducibility of findings. If you torture the data long enough, it will confess to anything.
Whether we go big or go small with our data, I suspect we will run into problems either way trying to decipher neuroimaging data for most psychiatric disorders. This is where I hope autoimmune biomarkers will provide some progress (e.g., anti-NMDAR). This has already helped advanced care for individuals with atypical presentations of psychosis, who would probably have instead been diagnosed with "schizophrenia" 30 years ago. Hopefully through more understanding in this lens, the false construct of schizophrenia can slowly be chipped away at.
I pretty much agree with everything you've said here. Though worth noting that Dosenbach's homunculus study also had a tiny sample size. Maybe if the questions/methods are super-precise, we can sacrifice the generalisability of larger, representative samples. I think you're right that there isn't an obvious immediate clinical application.
So how could Kraepelin describe dementia praecox when cannabis was not yet consumed in Central Europe?
I don’t think anyone argues that cannabis is necessary for the development of schizophrenia, but it is a risk factor
Excellent discussion!
In the 60s and 70s there were a large number of findings on atypical phenylketonuria (42 forms). In the 80s we found that these explain ¾ of all chronic psychoses - until the pharmaceutical companies stifled all research with serotonin
You're looking in the wrong place