In this study we used our novel diffusion imaging fiber cluster tractography method to assess the organization of frontostriatal brain wiring in EP-NA subjects and in HCs. We had four major findings. First, we found that the overall F-Cd wiring pattern, bilaterally, in both HCs and EP-NAs, deviated from a strictly topographic organization primarily driven by a cluster from IFG, pars triangularis. This was shown by non-linear, convex curves between inter-cluster cortical distances and caudate distances driven by the results from 10 cluster pairs in the left (LH) and right hemispheres (RH) in both HCs and in EP-NAs, all of which included a cluster coming from IFG, pars triangularis. Of note, however, we found a group difference such that in the RH in EP-NAs, the convex curve was more flattened. Second, for both groups in both hemispheres, we found certain clusters that showed significantly greater cluster pair convergence. More specifically, we found FC-caudate cluster projections with significantly more convergent patterns originating from PFC ventrolateral, dorsolateral, and orbitofrontal subregions Third, we found a more convergent FC caudate wiring pattern in the RH in HCs compared to EP NAs. More specifically, we showed that there was a greater number of FC-caudate cluster pairs with a more convergent projection pattern in HCs vs EP-NA subjects which was significant in the right, but not left, hemisphere. Fourth, we showed a significant group by fiber cluster pair interaction for 2 right hemisphere fiber clusters projecting from the frontal pole and rostral middle frontal gyrus (cluster 5) and from rostral middle frontal gyrus and inferior frontal gyrus, pars orbitalis (cluster 11), respectively.
Our finding showing a bilateral pattern of more localized deviation from a general topographic pattern of connectivity between the FC and caudate is both a replication and an extension of our prior study in healthy subjects (Levitt, 2021). We conclude that such an anatomical pattern of connectivity is generally present in human brains and thus might be considered a transdiagnostic feature of brain organization. Further, we surmise that regions that deviate from a strictly topographic pattern of connectivity (i.e., either increased or decreased convergence), add greater circuit complexity and both promote greater cross-talk and circuit integration at the level of the striatum. Despite some similarities between groups in connectivity patterns between frontal cortex and caudate, our findings also importantly suggest group differences in the degree of convergence of such connectivity, what we call the CQ score, which occur both at an overall level of organization as well as at a more localized level. Specifically, we find in pairwise comparisons of all 136 clusters that more clusters in HCs are convergent in HCs than in EP-NAs which is significant in the right hemisphere. Also, we find that cluster 5, coming from the frontal pole and rostral middle frontal gyrus and the contiguous cluster 11, coming from the right hemisphere inferior frontal gyrus, pars orbitalis and rostral middle frontal gyrus, are the 2 localized subregions of the PFC whose patterns of projecting streamlines, with all other clusters, differ between EP-NA and HC subjects. Such specific clusters can be conceived of as specific subcircuits within the total PFC-caudate circuitry. Our method, thus, allows for a highly refined way of measuring localized brain wiring deviations from a healthy control pattern. These FC regions and the subcircuits projecting from them, as discussed below, subserve higher cognitive functions, such as executive functions, impaired in schizophrenia.
Our finding is novel as it isolates a specific brain circuit (the FC-caudate loop within the cortico-basal ganglia circuitry) and demonstrates using dMRI tractography in vivo group differences between EP-NA subjects and HCs. Such a finding is consistent with current thoughts that SZ is caused by dysconnectivity in brain circuits. For example, multiple studies have shown that functional and structural connectivity is disrupted in SZ 14, 15, 17, 49.
As brain wiring occurs early in development, its disruption comports well with a neurodevelopmental hypothesis of schizophrenia e.g., 8, 11, 50. Further, as macroscale (i.e., long tract) brain wiring is established developmentally and then is enduring 51, dMRI tractography measures of brain wiring should serve as strong candidate trait biomarkers for disorders such as SZ with abnormal brain wiring e.g., 30. Moreover, genes affecting neuronal migration and axonal growth which can disrupt white matter long tract brain connectivity, have been found to be associated with schizophrenia 52, 53. Further, prenatal stress, a risk factor for SZ, as shown in a non-mammalian vertebrate model via maternal immune activation, can influence circuit formation and normal axonal development 54.
With regard to prefrontostriatal brain wiring development in non-human primates, autoradiographic tract tracing studies in monkeys have shown a distinct pattern of corticostriatal connectivity that is established in the final third of pregnancy prior to birth 51. Importantly, this pattern can be shown in newborn monkeys as well as in adult monkeys 55. More specifically, it has been shown that PFC-Cd fibers terminate in a pattern in which terminals are concentrated in patches surrounded by areas without PFC input, described as fenestrated by Goldman 55, and that a transformation from a diffuse pattern of distribution during early gestation to a fenestrated pattern occurs by the final third of pregnancy 51, see Fig. 6.. As adult monkeys continue to show the fenestrated pattern 55, the above strongly implies that in monkeys after the fenestrated pattern emerges during the final third of pregnancy, it persists into adulthood. Thus, the above monkey data suggest that our finding of group difference in FC-Caudate wiring patterns between HCP-EP subjects and HCs reflects wiring patterns that emerged prenatally and persisted throughout postnatal development into adulthood.
The organization of corticostriatal anatomic connectivity has been thoroughly investigated through the use of animal tract tracing studies and in human brain imaging studies 22, 30, 56. Animal tract tracing studies have shown projection zone overlap in the striatum of cortical projections e..g., 21, 57, 58. For example, in a more recent monkey tract tracing study, Averbeck et al., 21, found the pattern of corticostriatal connectivity deviated from a strictly topographic one. They compared the distance between pairs of injection sites in the frontal cortex in monkeys with the degree of overlap in the projection zones of these cortical injection sites. They found an exponential decrease in overlap in striatal projection zones as a function of greater distance between pairs of injection sites. Such non-human primate studies suggest that projection zone overlap is a characteristic anatomic feature of corticostriatal connectivity. In healthy human subjects, Draganski et al. 22, using probabilistic diffusion imaging, reported projection zone overlap in the striatum coming from prefrontal, premotor and motor cortices. Although our data do not determine where fibers terminate inside the striatum, we interpret our convergence measures to reflect a pattern of projection convergence from the prefrontal cortex to the caudate as similar to a pattern of projection zone overlap described by Averbeck et al. 21 and Dranganski et al. 22.
The cortico-basal ganglia circuitry has been described to influence a number of important higher cognitive functions, in addition to its traditional role in influencing motor activity. As our data shows clusters 5 and 11 in the right hemisphere differentiate groups, it is of interest to review the function of the subregions from which these clusters project, i.e., the frontal pole, and rMFG (cluster 5), and the rMFG and IFG, pars orbitalis (cluster 11).
The rMFG is located in the dorsolateral PFC. It is a critical component of the frontoparietal control network and subserves processes that include goal directed behavior, cognitive flexibility, such as mental set shifting, and working memory e.g., 20, 59, 60, 61. The IFG has been shown to be engaged by word retrieval and the updating of working memory 62. In addition, the IFG, in particular in the right hemisphere, has been shown to subserve inhibitory control. For example, damage to the right hemisphere IFG in human subjects and rodents has been shown to interfere with inhibitory control 63–65. Further, the frontal pole has been described as involved in cognitive functions including multi-tasking, prospective memory, and mentalizing (i.e., theory of mind), functions which have been subsumed under the term metacognition e.g., 65, 66. Deficits in such functions might cause significant difficulties in social cognition and employability, skills deficient in patients with schizophrenia e.g., 67, 68. Of note, impairments in the above higher cognitive functions have been described in schizophrenia 1, 29, 69–72.
Limitations of the paper include that the design of the study is a cross-sectional one. The idea that our brain wiring measures reflect normal neurodevelopment and its deviations should be confirmed in longitudinal studies across the lifespan. Other limitations include that medication and illness chronicity confounds cannot be ruled out as many of the patients were receiving antipsychotics and illness durations were variable. A further potential limitation is that individual streamlines within fiber clusters need not terminate directly onto the caudate in order to be counted in the endpoint calculations as described above in the Methods section. Lastly, we acknowledge the risk of false negative and false positive streamlines using dMRI tractography e.g., 73, 74. For future studies, it will be important to apply these measures in subjects over the lifespan, from early childhood to old age, to test their stability. Further, we plan to explore this circuitry in other neuropsychiatric disorders, such as early psychosis affective subjects, and to explore sex as a potential variable affecting brain wiring. Finally, FC-caudate brain wiring behavioral associations should also be explored.
In summary, employing a novel use of dMRI tractography, we found for both HCs and EP-NAs that the overall FC-caudate wiring pattern similarly deviated from a strictly topographic relationship and had similar clusters that projected to the caudate in a significantly convergent pattern of connectivity. Conversely, we found an overall significantly more convergent pattern of connectivity in HCs in the right hemisphere and that 2 specific clusters from selective PFC subregions in the right hemisphere significantly differed in their pattern of connectivity between HC and EP-NAs. We surmise that regions showing group differences impact certain higher cognitive functions disrupted in schizophrenia including cognitive control, inhibition and metacognition. Lastly, we believe the importance of our brain wiring measures is that they reflect trait biomarkers which can help to identify subjects with schizophrenia, early in their development, who would benefit from early treatment intervention.
Table 1. Demographic, Neuropsychological and Clinical measures
|
|
EP-NA Subjects (N = 108)a
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HC Subjects (N = 56)a
|
|
|
|
Mean
|
SD
|
Mean
|
SD
|
df
|
t-test
|
χ2
|
p-value
|
Age (years)
|
22.5
|
3.5
|
23.8
|
4.0
|
1, 162
|
2.24
|
|
0.03*
|
Males/Females
|
79/29
|
|
37/19
|
|
1,164
|
|
0.89
|
0.34
|
FSIQb
|
98.8 (n = 106)
|
16.6 (n = 106)
|
115.4 (n = 55)
|
11.0
(n = 55)
|
1, 156.4
|
7.51
|
|
< 0.001**
|
Vocabulary T Scorec
|
51.27 (n = 106)
|
12.54 (n = 106)
|
60.27 (n = 55)
|
8.79 (n = 55)
|
1,145.77
|
5.27
|
|
< 0.001**
|
Duration of illness (days)
|
652.22
(n = 104)
|
466.66
(n = 104)
|
NA
|
|
|
|
|
|
Antipsychotic Medication Dosage (CPZ equivalent for lifetime reported as mg/day)d
|
323.75
(n = 84)
|
209.68
(n = 84)
|
NA
|
|
|
|
|
|
Education level 1e
|
n = 24
|
n = 2
|
NA
|
Education level 2f
|
n = 77
|
n = 24
|
NA
|
Education level 3g
|
n = 5
|
n = 22
|
NA
|
Education level 4h
|
n = 2
|
n = 8
|
NA
|
PANNS Positive Marderi
|
15.45
(n = 103)
|
4.54
|
NA
|
NA
|
PANNS Negative Marderj
|
12.97
(n = 101)
|
5.18
|
|
|
DSMV-TR Diagnosis subtype:
schizophrenia
|
n = 56
|
NA
|
DSMV-TR Diagnosis subtype:
schizoaffective
|
n = 20
|
NA
|
DSMV-TR Diagnosis subtype:
schizophreniform
|
n = 8
|
NA
|
DSMV-TR Diagnosis subtype:
Other psychosesk
|
n = 7
|
|
aThe sample size (n) differs among variables owing to unavailability of data in some participants. bFSIQ= Composite Score Estimate (based on Wechsler Abbreviated Scale of Intelligence – second Edition (WAS-II)). cVocabulary T Score (based on (WASI-II) d79 Patients received neuroleptic medication (lifetime); 5 patients received no neuroleptic medication (lifetime). No data was unavailable for 24 subjects. eEducation level 1: <High School Degree; fEducation level 2: High School degree, GED, Associates Degree or Some University Courses; gEducation level 3: Bachelor’s degree, Some Graduate level courses, Doctoral level courses; hEducation level 4: Completed an advanced degree (Master’s Degree and beyond). iPANSS Positive Marder - Positive and Negative Syndrome Scale with Marder Factors 75. jPANSS Negative Marder - Positive and Negative Syndrome Scale with Marder Factors. kOther psychoses include other specified psychosis, delusional disorder, brief psychotic disorder. NA = data not applicable. CPZ = Chlorpromazine. DSMV-TR = Diagnostic and Statistical Manual of Mental Disorder, Fifth Edition, Text Revised. DSMV-TR subtype data was unavailable for 17 subjects.
|
*p < 0.05, **P < 0.001
|